Abstract: Systems and methods of adaptive control for uncertain nonlinear multi-input multi-output systems in the presence of significant unmatched uncertainty with assured performance are provided. The need for gain-scheduling is eliminated through the use of bandwidth-limited (low-pass) filtering in the control channel, which appropriately attenuates the high frequencies typically appearing in fast adaptation situations and preserves the robustness margins in the presence of fast adaptation.

Abstract: A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values for motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.

Abstract: Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.

Abstract: In various energy systems which handle a plurality of types of energy, a method and device for operation scheduling for energy storage equipment are provided, which determines optimal operation of an energy storage equipment and improves the efficiency of the energy system overall. An energy storage equipment operation scheduling part 13 creates an energy storage equipment operation schedule, an energy generation equipment modified operation schedule, and an energy storage equipment modified operation schedule, based on three information items, which are the energy demand forecast information stored in a storage part D1, the energy generation equipment operation schedule stored in a storage part D2, and equipment connection information, and sends the created schedules to the equipment controller 20.

Abstract: A prediction method that estimates the real-time position of a mobile device based on previously observed data is provided. The present invention can be used in real-time navigation, including providing real-time alerts of an upcoming destination and notifications of emergency events in close geographic proximity. The prediction method utilizes neural networks and/or functions generated using genetic algorithms in estimating the mobile device's real-time position. The prediction method provides reliable Location-Based Services (LBS) in events where traditional positioning technologies become unreliable. It is also seamless, as the user remains unaware of any interruption in accessing the positioning technology.

Abstract: A method for approximation of optimal control for a nonlinear discrete time system in which the state variables are first obtained from a system model. Control sequences are then iteratively generated for the network to optimize control variables for the network and in which the value for each control variable is independent of the other control variables. Following optimization of the control variables, the control variables are then mapped onto a recurrent neural network utilizing conventional training methods.

Abstract: The present invention relates to a method for controlling a processing operation of a workpiece by means of a Reinforcement Learning (RL) agent unit, comprising the steps of: (a) observing an interaction zone in the workpiece by means of at least one radiation sensor to generate at least one sensor signal st, wherein the workpiece is processed using an actuator having an initial actuator value at; (b) determining a basis function ?(st) from the set of sensor signals st; (c) determining a reward function rt giving the probability of good results of the processing operation; (d) choosing a next actuator value at+1 on the basis of a policy ? depending on the reward function rt and the basis function ?(st); and (e) repeating the steps (a) to (d) for further time points to perform a RL controlled processing operation.

Abstract: A method and system for adaptive auto-encoders is disclosed. An input audio training signal may be transformed into a sequence of feature vectors, each bearing quantitative measures of acoustic properties of the input audio training signal. An auto-encoder may process the feature vectors to generate an encoded form of the quantitative measures, and a recovered form of the quantitative measures based on an inverse operation by the auto-encoder on the encoded form of the quantitative measures. A duplicate copy of the sequence of feature vectors may be normalized to form a normalized signal in which supra-phonetic acoustic properties are reduced in comparison with phonetic acoustic properties of the input audio training signal. The auto-encoder may then be trained to compensate for supra-phonetic features by reducing the magnitude of an error signal corresponding to a difference between the normalized signal and the recovered form of the quantitative measures.

Abstract: One or more techniques are provided for the sharing of variable trajectories between multiple controllers in a control system. In general, the present technique is based upon the sharing of predicted desired variable trajectories between multiple controllers requiring a common input variable. In one embodiment, a common input variable is shared between two controllers, wherein one controller is tuned to have dominant control with regard to the shared variable. Each controller determines and communicates their own desired trajectory for the shared variable to the other controller and in doing so, the constraints associated with each controller are shared and honored by the other controller. Based on the communicated trajectories, the dominant controller may exercise control over the shared variable, thus improving overall process control.

Abstract: The electric power supply and demand control device judges whether or not electric power shortage is occurred or whether or not electric power surplus is occurred in the electric power supplier and demander provided with the electric power supply and demand control device based on data on total electric energy, the amount of maximum electric power demanded, and the amount of total electric power demanded of the following day in each electric power supplier and demander, predicted by a neural network; receives electric power from other electric power suppliers and demanders provided with the power generation devices and/or the electrical storage devices in the case where electric power shortage is occurred in the electric power supplier and demander; and controls to deliver electric power to other electric power suppliers and demanders in the case where electric power surplus is occurred in the electric power supplier and demander.

Abstract: A method and system for estimating context offsets for run-to-run control in a semiconductor fabrication facility is described. In one embodiment, contexts associated with a process are identified. The process has one or more threads, and each thread involves one or more contexts. A set of input-output equations describing the process is defined. Each input-output equation corresponds to a thread and includes a thread offset expressed as a summation of individual context offsets. A state-space model is created that describes an evolution of the process using the set of input-output equations. The state-space model allows to estimate individual context offsets.

Abstract: A system for monitoring an industrial process and taking action based on the results of process monitoring. Actions taken may include process control, paging, voicemail, and input for e-enterprise systems. The system includes an input module for receiving a plurality of parameters from a process for manufacture of a substance or object. The system also includes a library module. The library module includes a plurality of computer aided processes. Any one of the computer aided processes is capable of using each of the plurality of parameters to compare at least two of the plurality of parameters against a training set of parameters. The training set of parameters is generally predetermined. The computer aided process is also capable of determining if the at least two of the plurality of parameters are within a predetermined range of the training set of parameters. Additionally, the system includes an output module for outputting a result based upon the training set and the plurality of parameters.

Abstract: A control system (1) for a complex process, particularly for controlling a combustion process in a power plant, a waste incinerator plant, or a cement plant, has a controlled system (14) and at least one controller (36), wherein the control system (1) is divided hierarchically into various levels (10, 20, 30, 40). The first level (10) represents the complex, real process to be controlled and is implemented by the controlled system (14). The second level (20) represents an interface to the process and is implemented by a process control system. The third level (30) represents the control of the process and is implemented by the at least one active controller (36). The fourth level (40) represents a superordinate overview and is implemented by a principal controller (44).

Abstract: Real-time condition-based analysis is performed on a machine for providing diagnostic and prognostic outputs indicative of machine status includes a signal processor for receiving signals from sensors adapted for measuring machine performance parameters. The signal processor conditions and shapes at least some of the received signals into an input form for a neural network. A fuzzy adaptive resonance theory neural network receives at least some of the conditioned and shaped signals, and detects and classifies a state of the machine based upon the received conditioned and shaped signals, and upon a predetermined ontology of machine states, diagnostics, and prognostics. The neural network can also determine from the machine state a health status thereof, which can comprise an anomaly, and output a signal representative of the determined health status. A Bayesian intelligence network receives the machine state from the neural network and determines a fault probability at a future time.

Abstract: The present invention relates to a robot and a control method adapted for the robot. The method includes a) measuring time; b) performing a normal biological action when the time reaches a beginning time of a biological function; c) judging whether an instruction is received while during the biological function; d) if the instruction is received while during the biological function, fetching an action adjustment parameter of the biological function and performing an output; e) after the output is finished, resuming performing the normal biological action; and f) stopping the normal biological action when the time reaches an ending time of the biological function.

Abstract: An industrial process control transmitter includes a first input configured to couple to a first sensor, and a second input configured to couple to a second sensor. Measurement circuitry is configured to couple to the first and second sensors and provide an output related to a sensed process variable. A multiplexer is configured to selectively couple the first and second sensors to the measurement circuitry. An equalizer circuit is coupled to the first and second sensors and configured to equalize a voltage potential between the first sensor and the measurement circuitry.

Abstract: An input image is received, represented by a matrix D having a first number of dimensions. Each of the first number of dimensions may represent or correspond to a portion of the image. A metric objective may be identified. A dimensional reduction on the matrix D may then be performed that optimize the metric objective, so that a matrix d of a second number of dimensions is identified to represent the input image, where the second number of dimensions is less than the first number of dimensions.

Abstract: A method is used for providing sensing data to a control system of a machine. The method may include providing a plurality of virtual sensors, each of which may have a model type, at least one input parameter, and at least one output parameter. The method may also include integrating the plurality of virtual sensors into a virtual sensor network; determining interdependencies among the plurality of virtual sensors; and obtaining operational information of the plurality of virtual sensors. Further, the method may include recording measurement data and performance information of the virtual sensor network and the plurality of virtual sensors; and generating one or more calibration certificate of the virtual sensor network based on the operational information, the measurement data, and the performance information.

Abstract: A plant control apparatus has a measurement signal DB, an operation signal DB, a numerical value analysis executing unit, a numerical value analysis result DB, a model for estimating a value of a measurement signal for an operation signal, learning unit using the model, a learning information DB, a control logic DB for information used by an operation signal generating unit, a knowledge DB for plant running characteristics, and an analyzing unit processing information in the numerical value analysis result DB by information in the knowledge DB, learning information DB, operation signal DB, and measurement signal DB, wherein the analyzing unit includes at least one of a learning base analyzing unit checking adequacy of an operation method, a signal analyzing unit for evaluating an effect of the operation signal upon the plant and presence/absence of an abnormal signal, and a knowledge DB updating unit correcting information in the knowledge DB.

Abstract: A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.

Abstract: The control optimization method for helicopters carrying suspended loads during hover flight utilizes a controller based on time-delayed feedback of the load swing angles. The controller outputs include additional displacements, which are added to the helicopter trajectory in the longitudinal and lateral directions. This simple implementation requires only a small modification to the software of the helicopter position controller. Moreover, the implementation of this controller does not need rates of the swing angles. The parameters of the controllers are optimized using the method of particle swarms by minimizing an index that is a function of the history of the load swing. Simulation results show the effectiveness of the controller in suppressing the swing of the slung load while stabilizing the helicopter.

Abstract: The fuzzy logic-based control method for helicopters carrying suspended loads utilizes a controller based on fuzzy logic membership distributions of sets of load swing angles. The anti-swing controller is fuzzy-based and has controller outputs that include additional displacements added to the helicopter trajectory in the longitudinal and lateral directions. This simple implementation requires only a small modification to the software of the helicopter position controller. The membership functions govern control parameters that are optimized using a particle swarm algorithm. The rules of the anti-swing controller are derived to mimic the performance of a time-delayed feedback controller. A tracking controller stabilizes the helicopter and tracks the trajectory generated by the anti-swing controller.

Abstract: A method of determining complete sensor requirements for autonomous mobility of an autonomous system includes computing a time variation of each behavior of a set of behaviors of the autonomous system, determining mobility sensitivity to each behavior of the autonomous system, and computing a change in mobility based upon the mobility sensitivity to each behavior and the time variation of each behavior. The method further includes determining the complete sensor requirements of the autonomous system through analysis of the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior, wherein the relative magnitude of the change in mobility, the mobility sensitivity to each behavior, and the time variation of each behavior are characteristic of the stability of the autonomous system.

Abstract: A control system (1) for a complex process, particularly for controlling a combustion process in a power plant, a waste incinerator plant, or a cement plant, has a controlled system (14) and at least one controller (36), wherein the control system (1) is divided hierarchically into various levels (10, 20, 30, 40). The first level (10) represents the complex, real process to be controlled and is implemented by the controlled system (14). The second level (20) represents an interface to the process and is implemented by a process control system. The third level (30) represents the control of the process and is implemented by the at least one active controller (36). The fourth level (40) represents a superordinate overview and is implemented by a principal controller (44).

Abstract: Process control system for detecting abnormal events in a process having one or more independent variables and one or more dependent variables. The system includes a device for measuring values of the one or more independent and dependent variables, a process controller having a predictive model for calculating predicted values of the one or more dependent variables from the measured values of the one or more independent variables, a calculator for calculating residual values for the one or more dependent variables from the difference between the predicted and measured values of the one or more dependent variables, and an analyzer for performing a principal component analysis on the residual values. The process controller is a multivariable predictive control means and the principal component analysis results in the output of one or more scores values, T2 values and Q values.

Abstract: A method for collecting operational parameters of a motor may include controlling the energization of a phase winding of the motor to establish an operating point, monitoring operational parameters of the motor that characterize a relationship between the energization control applied to the motor's phase winding and the motor's response to this control, and collecting information of the operational parameters for the operating point that characterizes the relationship between the applied energization control and the motor's response. The collected information characterizing the relationship between the applied energization control and the motor's response may be employed by a neural network to estimate the regions of operation of the motor. And a system for controlling the operation of motor may employ this information, the neural network, or both to regulate the energization of a motor's phase winding during a phase cycle.

Abstract: A method is used for providing sensing data to a control system of a machine. The method may include providing a plurality of virtual sensors, each of which may have a model type, at least one input parameter, and at least one output parameter. The method may also include integrating the plurality of virtual sensors into a virtual sensor network; determining interdependencies among the plurality of virtual sensors; and obtaining operational information of the plurality of virtual sensors. Further, the method may include determining a first condition under which the virtual sensor network is unfit to provide one or more virtual sensor output parameter to the control system based on the determined interdependencies and the operational information; and presenting the determined first condition to the control system.

Abstract: A method for recognition between a first and second object represented by at least one first image and at least one second image, includes defining rectangular assemblies of random pixels; filtering the first image with first n filters obtained from the assemblies of pixels to obtain n first filtered matrices; classifying the n first filtered matrices by providing a first center and a first radius within a space of N dimensions; filtering the second image with the first n filters to obtain n second filtered matrices; classifying the n second filtered matrices by providing a second center within the space of N dimensions; and comparing the first center and first radius with the second center.

Abstract: In a control loop for regulating a combustion process in a plant (e.g., a power-generating plant, a waste incineration plant or a cement works) having a controlled system for converting material by way of the combustion process while supplying air, with at least one flame body being formed, and having at least one observation device for imaging the flame body, other sensors for determining the input data, at least one adjustment device that can be controlled by output data for supplying at least material and/or air, and a computer for evaluating the input data in relation to target values and for determining the output data by using a current process model, the computer has a feature extraction module that extracts features from the input data using an information measure. The features are informative for the target values, and are for use in an alternative process model.

Abstract: A method for fetal weight estimation wherein fetal biometric parameters are ultrasonically measured and further physiological and phenomenological pregnancy parameters are determined for a plurality of sample cases. The fetal weight for each of the sample cases is then determined by precision weighing at birth. A database is then created based on the known sample cases and mathematical prediction models are generated from the database. For the case under examination for which fetal weight is to be predicted, the fetal biometric parameters are ultrasonically measured and further physiological and phenomenological pregnancy parameters are determined. The fetal weight of the case under examination is predicted by using a mathematical prediction model and a multinormal probabilistic model is used as a mathematical model.

Abstract: A learning controller overcomes tuning problems in vehicle simulation programs by estimating requisite vehicle-specific parameters, effectively learning from its mistakes, as the vehicle is automatically driven around a track. After a sufficient period of calibration, the learned parameters are automatically saved to a car-specific file. The file parameters may be loaded in the controller in the future to optimally control a vehicle without the need to re-run the learning procedure.

Abstract: A method for providing a virtual sensor network based system. The method may include obtaining project data descriptive of a virtual sensor network to be used in a control system of a machine; establishing a virtual sensor network including a plurality of virtual sensors based on the project data. Each virtual sensor may have a model type, at least one input parameter, and at least one output parameter. The method may also include recording model information, measurement data, and performance information of the virtual sensor network including the plurality of virtual sensors; creating one or more calibration certificates of the virtual sensor network including a plurality of virtual sensors based on the model information, the measurement data, and the performance information; and generating a documentation package associated with the virtual sensor network.

Abstract: A system for monitoring an industrial process and taking action based on the results of process monitoring. Actions taken may include process control, paging, voicemail, and input for e-enterprise systems. The system includes an input module for receiving a plurality of parameters from a process for manufacture of a substance or object. The system also includes a library module. The library module includes a plurality of computer aided processes. Any one of the computer aided processes is capable of using each of the plurality of parameters to compare at least two of the plurality of parameters against a training set of parameters. The training set of parameters is generally predetermined. The computer aided process is also capable of determining if the at least two of the plurality of parameters are within a predetermined range of the training set of parameters. Additionally, the system includes an output module for outputting a result based upon the training set and the plurality of parameters.

Abstract: Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. These features may be employed alone or in combination to offer improved usability and computational efficiency for real time control and monitoring of a semiconductor manufacturing process. More generally, these techniques may be usefully employed in a variety of real time control systems, particularly systems requiring complex scheduling decisions or heterogeneous systems constructed of hardware from numerous independent vendors.

Abstract: A method for controlling an industrial automation device or process including a control unit, at least one actuator, and at least one device arranged for wireless communication with the control unit. The method determines characteristics of the wireless transmissions used to communicate sensor and/or actuator data to the control unit. The method, a system and a graphic interface enable a user to select a control strategy dependent on a value or values of the characteristics of the wireless communications.

Abstract: A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.

Abstract: A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOx and SO2, into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.

Abstract: A method for configuring a machine learning model for use in a motion characterisation process. The method comprises, in a processing system, acquiring user characterisations for respective portions of at least one video sequence and configuring the model using the user characterisations and at least one property associated with the respective portions. Any inconsistency in the user characterisations is then determined, using the model, with an indication of the inconsistency being displayed. This allows the user to select at least one option for addressing the inconsistency, with the model being reconfigured based on the selected at least one option.

Abstract: The invention comprises apparatuses and methods for providing the capability to stabilize and control a non-minimum phase, nonlinear plant with unmodeled dynamics and/or parametric uncertainty through the use of adaptive output feedback. A disclosed apparatus can comprise a reference model unit for generating a reference model output signal ym The apparatus can comprise a combining unit that combines and differences a plant output signal y of a non-minimum phase plant for which not all of the states can be sensed, and a plant output signal y, to generate an output error signal {tilde over (y)}. The apparatus can further comprise an adaptive control unit for generating an adaptive control signal uad used to control the plant.

Abstract: A neural network in a speech-recognition system has computing units organized in levels including at least one hidden level and one output level. The computing units of the hidden level are connected to the computing units of the output level via weighted connections, and the computing units of the output level correspond to acoustic-phonetic units of the general vocabulary. This network executes the following steps: determining a subset of acoustic-phonetic units necessary for recognizing all the words contained in the general vocabulary subset; eliminating from the neural network all the weighted connections afferent to computing units of the output level that correspond to acoustic-phonetic units not contained in the previously determined subset of acoustic-phonetic units, thus obtaining a compacted neural network optimized for recognition of the words contained in the general vocabulary subset; and executing, at each moment in time, only the compacted neural network.

Abstract: A method is provide for providing sensors for a machine. The method may include obtaining data records including data from a plurality of sensors for the machine and determining a virtual sensor corresponding to one of the plurality of sensors. The method may also include establishing a virtual sensor process model of the virtual sensor indicative of interrelationships between at least one sensing parameters and a plurality of measured parameters based on the data records and obtaining a set of values corresponding to the plurality of measured parameters. Further, the method may include calculating the values of the at least one sensing parameters substantially simultaneously based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model and providing the values of the at least one sensing parameters to a control system.

Abstract: A method for a computer-aided control of a technical system is provided. The method involves use of a cooperative learning method and artificial neural networks. In this context, feed-forward networks are linked to one another such that the architecture as a whole meets an optimality criterion. The network approximates the rewards observed to the expected rewards as an appraiser. In this way, exclusively observations which have actually been made are used in optimum fashion to determine a quality function. In the network, the optimum action in respect of the quality function is modeled by a neural network, the neural network supplying the optimum action selection rule for the given control problem. The method is specifically used to control a gas turbine.

Abstract: A brain-based device (BBD) for moving in a real-world environment has sensors that provide data about the environment, actuators to move the BBD, and a hybrid controller which includes a neural controller having a simulated nervous system being a model of selected areas of the human brain and a non-neural controller based on a computational algorithmic network. The neural controller and non-neural controller interact with one another to control movement of the BBD.

Abstract: A system, method, and computer readable-media for creating a stable synthetic neural system. The method includes training an intellectual choice-driven synthetic neural system (SNS), training an emotional rule-driven SNS by generating emotions from rules, incorporating the rule-driven SNS into the choice-driven SNS through an evolvable interface, and balancing the emotional SNS and the intellectual SNS to achieve stability in a nontrivial autonomous environment with a Stability Algorithm for Neural Entities (SANE). Generating emotions from rules can include coding the rules into the rule-driven SNS in a self-consistent way. Training the emotional rule-driven SNS can occur during a training stage in parallel with training the choice-driven SNS. The training stage can include a self assessment loop which measures performance characteristics of the rule-driven SNS against core genetic code.

Abstract: A method of building a set of experimental prediction model that requires fewer experimental frequency, shorter prediction time and higher prediction accuracy by using the advantages of combining the experimental data of Taguchi method and neural network learning is disclosed. The error between the experimentally measured result of photolithography and the simulated result of the theoretical model of near field photolithography is set as an objective function of an inverse method for back calculating fiber probe aperture size, which is adopted in the following Taguchi experiment. The analytical result of Taguchi neural network model of the present invention proves that the Taguchi neural network model can provide more accurate prediction result than the conventional Taguchi network model, and at the same time, improve the demerit of requiring massive training examples of the conventional neural network.

Abstract: In one aspect, the present disclosure describes a power adjustment process. The process for power distribution regulation includes filtering data from electrical sensors to provide conditioned data representative of a portion of a power distribution grid and determining, by a controller and based in part on the conditioned data, when an increase or decrease in an output parameter from one regulator of a plurality of regulators in the power distribution grid will reduce system power consumption. The process also includes increasing or decreasing the associated output electrical parameter in response to the controller determining that such will reduce system power consumption.

Abstract: A controller is provided for directing control of a process performed to control an amount of a pollutant emitted into the air. The process has multiple process parameters (MPPs) The controller includes either a neural network process model or a non-neural network process model. Whichever type model is included, it will represent a relationship between one of the MPPs and other of the MPPs. The controller also includes a control processor having the logic to determine the validity of a measured value of the one MPP based on the one model. The control processor directs control of the process in accordance with the measured value of the one MPP only if the measured value of the one MPP is determined to be valid. On the other hand, if the measured value is determined to be invalid, the control processor may direct control of the process in accordance with an estimated value of the one MPP.

Abstract: According to one embodiment, a control device that controls operation of a system includes a first selecting module, a second selecting module, a control error measuring module, a determining module, and a control module. The first selecting module selects a first neural network from neural networks different in network configuration from each other. The second selecting module selects a second neural network different from the first neural network from the neural networks. The control error measuring module measures first control error in control by the first neural network and second control error in control by the second neural network. The determining module compares the first control error and the second control error measured by the control error measuring module, and determines a neural network with less control error. The control module controls the operation of the system by the neural network with less control error determined by the determining module.

Abstract: A work model (or an image) is displayed on an image plane of a robot simulator (201), and a measuring portion and a measuring method are designated (202, 203) and a work shape and a work loading state are designated (204), and then it is judged whether or not the measuring portion and the measuring method are good (205). When the measuring portion and the measuring method are good, a program is generated and the processing is completed (207, 208). When the measuring portion and the measuring method are not good, an alarm is given (206), and the continuation (207) or the repetition (201) of the processing is directed. At the time of analyzing the program, the loading (101), the analysis and display of the measuring portion and the measuring method (102, 103) and the work information (104) are designated, and then it is judged whether or not the measuring portion and the measuring method, which have been analyzed, are good (105).

Abstract: The present invention applies a slope predictive control method to a Variable Temperature Control (VTC) system, measuring the slope of the discharge temperature as a function of flow regulation device position, and using this slope to predict the position of the flow regulation device needed to achieve the desired discharge temperature. The present invention also monitors the response of the VTC over time and utilizes a self-learning algorithm to predict the response time of the system in order to determine when further control adjustments need to be taken.