Abstract: A controller for a plant having a fixed-weight recurrent neural network with at least one external input signal representative of a desired condition of the plant and actual condition of the plant, and an output connected as a control signal to the plant. The fixed recurrent neural network includes a set of nodes with fixed weight interconnections between the nodes and at least one feedback input interconnecting an output from at least one of the nodes to an input of at least one node. These nodes collectively determine the value of the output from the neural network as a function of the input signal and the feedback input. The controller also includes an adaptive neural network having a plurality of nodes with variable weight interconnections between the nodes. A cost input from the plant is connected to the adaptive neural network while an output from the adaptive neural network is coupled as a processed feedback signal to nodes of the fixed-weight recurrent neural network.

Abstract: A controller directs a process primarily performed to control emission of a particular pollutant into the air. The process has multiple process parameters (MPPs), including a parameter representing an amount of the particular pollutant. The controller includes either a neural network process model or a non-neural network process model. In either case, the model represents a relationship between a first of the MPPs and one or more of the other MPPs. The one or more other MPPs include a second of the MPPs which is other than the parameter representing the amount of the emitted particular pollutant. Also included is a processor configured with logic to estimate a value of the second MPP, and to direct control of the first MPP based on the estimated value of the second MPP and the model.

Abstract: A field mountable dedicated process diagnostic device is used for diagnosing operation of an industrial control or monitoring system. An input is configured to receive at least one process signal related to operation of the industrial process. A memory contains diagnostic program instructions configured to implement a diagnostic algorithm using the process signal. The diagnostic algorithm is specific to the industrial process. A microprocessor performs the diagnostic program instructions and responsively diagnoses operation of the process based upon the process signal.

Abstract: A method for the orthogonalization of initially overlapping vectors, the uses for which include the re-encoding and decoding of representations triggered by sensory arrays.

Abstract: A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Abstract: A temporary self-organizing map in which classes are associated with respective vector points is first derived by a first learning section of a data learning apparatus. Thereafter, by use of, for example, an improved method incorporating concept of vicinity learning intp learning vector quantization, a second learning section modifies the temporary self-organizing map and obtains a final self-organizing map. By use of the final self-organizing map thus derived, image meaning determining processing is performed.

Abstract: A control device for a plant includes a manipulation signal generation section for generating the manipulation signal to the plant, a model adapted to simulate a characteristic of the plant, a learning section for generating an input signal of the model so that an output signal obtained by the model simulating the characteristic of the plant satisfies a predetermined target, a learning signal generation section for calculating a learning signal in accordance with a learning result in the learning section, a manipulation result evaluation section for calculating a first deviation as a deviation between a first measurement signal of the plant obtained as a result of application of a certain manipulation signal to the plant and a target value of the measurement signal, and a second deviation as a deviation between a second measurement signal of the plant obtained as a result of application of an updated manipulation signal to the plant and the target value, and a correction signal generation section for generati

Abstract: One aspect of the present disclosure includes a method for a control system of a machine. The method may include establishing a virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine. The method may also include obtaining data and function information representing the virtual sensor model and converting the data information into fixed-point representation. Further, the method may include converting the function information into fixed-point representation and loading the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation.

Abstract: A quality prognostics system and a quality prognostics method for predicting the product quality during manufacturing processes are disclosed, wherein the current production tool parameters sensed during the manufacturing process and several previous quality data collected from the measurement tool are utilized to predict the future product quality, and a conjecture modeling step and prediction modeling step are performed respectively. The conjecture modeling step itself also can be applied for the purpose of virtual metrology. Further, a self-searching step and a self-adjusting step are performed for searching the best combination of various parameters/functions used by the conjecture algorithm or prediction algorithm; and meeting the requirements of new equipment parameters and conjecture/prediction accuracy.

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 self-developing device (1) capable of open-ended development makes use of a special motivational system for selecting which action should be taken on the environment by an associated sensory-motor apparatus (2). For a given candidate action, a motivational module (11) calculates a reward associated with the corresponding values that would be taken by one or more motivational variables that are independent of the nature of the associated sensory-motor apparatus. Preferred motivational variables are dependent on the developmental history of the device (1), and include variables quantifying the predictability, familiarity and stability of sensory-motor variables serving as the inputs to the device (1). The sensory-motor variables represent the status of the external environment and/or the internal resources (3) of the sensory-motor apparatus (2) whose behavior is controlled by the self-developing device (1).

Abstract: A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.

Abstract: Operation of a multi-variable process involves multidimensional representation of the value (p1-p12) of the process variables (P1-P12) according to individual coordinate axes, and an operational envelope (UB,LB) derived from a group of sets of values for the process and quality variables (P1-P12,Q1-Q2) accumulated from multiple, earlier operations of the process, defines an operating zone for the process and quality variables of the process. If the current value (p7) of any process variable (P7) goes outside the envelope, an envelope (UO,LB) for a different, wider grouping of the stored data-sets is displayed at least for the quality variables (Q1-Q2). A series of nested envelopes to provide stepwise enlargement of the operating zone may be provided, but non-nested envelopes can be used where there is clustering of acceptable values of process variables of the stored data-sets. The changes to control variables to bring the values of dependent variables within a best operating range can be determined.

Abstract: A method for creating a non-linear, stationary or dynamic overall model of a control variable of a combustion engine or partial systems thereof is based on simplified partial model functions that are used to determine in a weighted fashion at each desired operating point the total output quantities from the partial model function with an associated weighting function. The difference between the total output quantity and the real value is determined for all real operating points; and in areas of operating points with an absolute value of this difference that is above the preset value, a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value.

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 computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: The inventive system for estimating quantities of pollution compounds emitted in the exhaust gas of a diesel engine for a motor vehicle comprises means for regenerating a solid particle filter and an electronic control unit which manages the engine operation and is provided with one or several data memories. Said system also comprises one or several neurone networks (1) and networks (2) of input data representative for the engine operation and possibly for the vehicle motion, said data is available in the electronic control unit for managing the engine operation without an additional sensor. Said system also comprises means (4) for cumulating estimated quantities.

Abstract: A modulation recognition method and device for digitally modulated signals with multi-level magnitudes are provided. The modulation recognition method includes selecting plural quantization sizes used to construct plural statistic histograms related to the magnitude of a sequence of data, setting up an off-line processing to extract plural useful feature patterns for each modulation type of interest, receiving a sequence of samples of a modulated object signal and constructing plural statistic histograms related to the magnitude of these samples, and adopting a hierarchical classification method for modulation recognition. It can be applied to the adaptive-modulation communication system, software defined radio, digital broadcasting systems and military communication systems. It can also be integrated with modulation recognition techniques for other types of modulated signals to function in a universal demodulator.

Abstract: A method of malicious network activity detection. An intrusion detection system provides defense against distributed denial of service (DDOS) attacks through an efficient modeling process based on grey theory.

Abstract: A controlling system for a cardboard-sheet manufacturing apparatus includes an FF/FB controlling unit, a PID controller, and a knowledge database. The FF/FB controlling unit differentiates between compensation for a dynamic characteristic and that for a static characteristic based on the knowledge database, and switches between FF control and FB control. The PID controller operates based on a two-degree-of-freedom PID algorithm. The FF/FB controlling unit adjusts a feedback gain based on information stored in the knowledge database.

Abstract: A method of image processing using neural networks. Images to be processed and displayed are received. Each image is divided into sections, each comprising pixels represented by color values. A neural network training procedure is executed for the sections to obtain color value tables and mapping tables. Each color value table comprises colors represented by the color values. The mapping tables record the relations between each pixel and the colors in the color value tables. The color value tables and the mapping tables are recorded in an electronic device for image display.

Abstract: A constrained non-linear approximator for empirical process control is disclosed. The approximator constrains the behavior of the derivative of a subject empirical model without adversely affecting the ability of the model to represent generic non-linear relationships. There are three stages to developing the constrained non-linear approximator. The first stage is the specification of the general shape of the gain trajectory or base non-linear function which is specified graphically, algebraically or generically and is used as the basis for transfer functions used in the second stage. The second stage of the invention is the interconnection of the transfer functions to allow non-linear approximation. The final stage of the invention is the constrained optimization of the model coefficients such that the general shape of the input/output mappings (and their corresponding derivatives) are conserved.

Abstract: The present invention relates to a system and a method for developing an engine model. The system broadly comprises a module for generating a state variable model of an engine, which module receives a plurality of inputs to an engine representative of a particular flight condition and generates a set of estimated engine parameters representative of the model. The system further comprises a comparator for comparing the set of estimated engine parameters to a set of measured engine parameters for generating a set of residuals and an artificial neural network module to be trained and to be used in an implementation configuration after training has been completed. The artificial neural network receives the set of residuals and the engine inputs during a training phase and generates a set of estimated residuals representative of the engine condition.

Abstract: Test molding and mass-production molding are performed by an injection molding machine that includes a control apparatus in which neural networks are used. A quality prediction function determined based on the test molding is revised as necessary during mass-production molding.

Abstract: An object of the present invention is to realize an in-home network system that is capable of smoothly controlling an appliance even when a new kind of appliance that has not been obtained conventionally is connected to an in-home network or even when an unusual, special use form is set in a network. A controller of the present invention compares a device type of an appliance connected to an in-home network with a skeleton stored in a skeleton DB. When a skeleton rule in accordance with the appliance type is not stored in the skeleton DB, a request for acquisition of the skeleton rule of the appliance is transmitted to a service agent. In addition, when the location of an appliance is moved, the controller sends a request for transmission of a new rule format to be added due to the movement to the service agent.

Abstract: A system and method for implementing an indirect controller for a plant. A plant can be provided with both a direct controller and an indirect controller with a system model or a committee of system models. When the system model has sufficient integrity to satisfy the plant requirements, i.e., when the system model has been sufficiently trained, the indirect controller with the system model is automatically enabled to replace the direct controller. When the performance falls, the direct controller can automatically assume operation of the plant, preferably maintaining operation in a control region suitable for generating additional training data for the system model. Alternatively, the system model incorporates a committee of models. Various types of sources for errors in the committee of models can be detected and used to implement strategies to improve the quality of the committee.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: An apparatus and method is disclosed for automatically controlling single-input-multi-output (SIMO) systems or processes. The control output signals of a plurality of single-input-single-output (SISO) automatic controllers are combined by a combined output setter so that these SISO controllers are converted to a multi-input-single-output (MISO) automatic controller based on certain criteria; and its resulting controller output signal is able to manipulate only one actuator to control a plurality of continuous process variables or attempt to minimize a plurality of error signals between the setpoints and their corresponding process variables.

Abstract: A method and apparatus for efficient implementation and evaluation of state machines and programmable finite state automata is described. In one embodiment, a state machine architecture comprises a plurality of node elements, wherein each of the plurality of node elements represents a node of a control flow graph. The state machine architecture also comprises a plurality of interconnections to connect node elements, a plurality of state transition connectivity control logic to enable and disable connections within the plurality of interconnections to form the control flow graph with the plurality of node elements, and a plurality of state transition evaluation logic coupled to the interconnections and operable to evaluate input data against criteria, the plurality of state transition evaluation logic to control one or more state transitions between node elements in the control flow graph.

Abstract: A neural network controller in parallel with a proportional-plus-integral (PI) feedback controller in a control system. At least one input port of the neural network for receiving an input signal representing a condition of a process is included. A first set of data is obtained that includes a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of the process. The process/plant condition signals generally define the process/plant, and may include one set-point as well as signals generated using measured systems variables/parameters. In operation, the neural network contributes to an output of the PI controller only upon detection of at least one triggering event, at which time a value of the first set of data corresponding with the condition deviation is added-in thus, contributing to the proportional-plus-integral feedback controller.

Abstract: At least one of the multiple process parameters (MPPs) is a controllable process parameter (CTPP) and one is a targeted process parameter (TPP). The process also has a defined target limit (DTV) representing a first limit on an actual average value (AAV) of the TPP. A first logical controller predicts future average values (FAVs) of the TPP based on the AAVs of the TPP over a first prior time period and the DTV. A second logical controller establishes a further target limit (FTV) representing a second limit on the AAV of the TPP based on one or more of the predicted FAVs, and also determines a target set point for each CTPP based on the AAVs of the TPP over a prior time period and the FTV. The second logical controller directs control of each CTPP in accordance with the determined target set point.

Abstract: An adaptive learning method for automated maintenance of a neural net model is provided. The neural net model is trained with an initial set of training data. Partial products of the trained model are stored. When new training data are available, the trained model is updated by using the stored partial products and the new training data to compute weights for the updated model.

Abstract: A method for optimizing the configuration of a pick-and-place machine utilizes a genetic algorithm that creates an initial population of possible configurations and selects an optimum configuration based upon lowest cycle time. The method then creates a next generation by selecting possible configurations from the prior generation and randomly mutating instructions. The method compares the mutated configuration having the lowest cycle time from the next generation and the configuration having the lowest cycle time from the prior generation, and selects the optimum configuration. The steps are repeated to evaluate additional generations of mutated configurations.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: In a method for controlling a thermodynamic process, in particular a combustion process, in which the system status (st) is measured, compared with optimization targets (rj), and in which suitable setting actions (ai) are performed in the system for controlling it, a process model (PM) is determined that is independent of the optimization targets (rj) and which describes the effects of actions (at) on the system status (st), and in which a situational evaluation (SB) that is independent of the process model (PM) evaluates the system status (st) by means of quality functions (ut) with regard to the optimization targets (rj).

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 method is provided, the method comprising sampling at least one parameter characteristic of processing performed on a workpiece in at least one processing step, and modeling the at least one characteristic parameter sampled using an adaptive sampling processing model, treating sampling as an integrated part of a dynamic control environment, varying the sampling based upon at least one of situational information, upstream events and requirements of run-to-run controllers. The method also comprises applying the adaptive sampling processing model to modify the processing performed in the at least one processing step.

Abstract: A plant is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) models the plant by providing a predicted output which is combined with a desired output to generate an error that is back propagated through an inverse control network to generate a control error signal that is input to a distributed control system to vary the control inputs to the plant in order to change the output y(t) to meet the desired output. The inverse model represents the dependencies of the plant output on the control variables parameterized by external influences to the plant.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Abstract: An adaptive process controller drives a process variable to be substantially equivalent to a set point and adapts the controller gain, the controller reset, and/or the controller rate, based on model free adaptation. The adaptive controller combines a controller gain computed from an oscillation index with a controller gain computed from a steady state estimate and that adapts the controller reset/rate by forcing the ratio of two of the controller proportional, integral or derivative terms to be equal to a predetermined value.

Abstract: The present invention is directed to a system, method, and apparatus for orienting images. A neural net is trained with images of known orientation and an indicator indicating such known orientation. Images of unknown orientation are then input to the neural net and the orientation is determined based on the output of the neural net.

Abstract: A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

Abstract: A method for computerized industrial process control provides computers networked to communicate with one another. Each computer active in the system is responsbile for at least a portion of the process and at least one decision for a process to be controlled and having an output. All activities are characterized by type, the types of activities forming a universal set including sensing facts, linking facts into a meaningful context, and evaluating meaning to formulate a decision. An entity responsible for an assigned decision conducts a series of activities selected from the three types, which may be applied recursively. Decisions are communicated between computers through the system to control the process. Producing output from the process follows according to a combination of decisions reported from each computer corresponding to a responsible person or other entity.

Abstract: An advanced control block that implements multiple-input/multiple-output control, such as model predictive control, within a process control system uses a compensation block or algorithm and a single control model based on a single process model to provide advanced control in a process having widely variable process delay. The compensation block changes the execution period of the advanced control block to account for changes in the one or more process variables responsible for the variable process delay, which eliminates the need to provide different advanced control models or control definitions for different operating regions for a process in the cases in which the delay in a process output is correlated to a measurable process or control variable.

Abstract: A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.

Abstract: A computer system linked by the internet to various remote waste water treatment facilities. The system receives real-time data from the facilities and analyzing the data to determine likely operational upsets and future effluent water quality. The computer system sends signals to a hierarchy of parties depending on the severity of predicted upsets problems and events. The computer also provides a probability distribution of such upsets and water quality and recommendations as how to adjust facility operating parameters to avoid or reduce the upsets to acceptable parameters and maintain effluent water quality parameters within preselected limits.

Abstract: A method of determining properties relating to the manufacture of an injection-molded article is described. The method makes use of a hybrid model which includes at least one neural network and at least one rigorous model. In order to forecast (or predict) properties relating to the manufacture of a plastic molded part, a hybrid model is used which includes: one or more neural networks NN1, NN2, NN3, NN4, . . . , NNk; and one or more rigorous models R1, R2, R3, R4, . . . , which are connected to one another. The rigorous models are used to map model elements which can be described in mathematical formulae. The neural model elements are used to map processes whose relationship is present only in the form of data, as it is typically impossible to model such processes rigorously. As a result, a forecast (or prediction) relating to properties including, for example, the mechanical, thermal and rheological processing properties and relating to the cycle time of a plastic molded part can be made.

Abstract: A method of predicting the properties (e.g., mechanical and/or processing properties) of an injection-molded article is disclosed. The method makes use of a hybrid model which includes at least one neural network. In order to forecast (or predict) properties with respect to the manufacture of a plastic molded article, a hybrid model is used in the present invention, which includes: one or more neural networks NN1, NN2, NN3, NN4, . . . , NNk; and optionally one or more rigorous models R1, R2, R3, R4, . . . , which are connected to one another. The rigorous models are used to map model elements which can be described in mathematical formulae. The neural networks are used to map processes whose relationship is present only in the form of data, as it is in effect impossible to model such processes rigorously. As a result, a forecast relating to properties including the mechanical, thermal and rheological processing properties and relating to the process time of a plastic molded article is obtained.

Abstract: The present invention provides a method and system for complex process optimization utilizing metrics, operational variables, or both, of one or more process steps and optimization of one or more of these process step parameters with respect to a cost function for the parameter. In one embodiment, the invention provides a scalable, hierarchical optimization method utilizing optimizations at one process level as inputs to an optimization of a higher or lower process level.

Abstract: Method for controlling and preconfiguring a steelworks or parts of a steelworks, the rolling stand or the rolling mill train being controlled and preconfigured by means of a model of the rolling stand or the rolling mill train, the model having at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, being varied.