Abstract: A method for estimating a state of a system including determining, during a current cycle of the system, an estimated state of the system based on currently measured values of the system, boundary conditions of the system, an accuracy of the currently measured values, and an estimated state from a preceding cycle. The method also determines an accuracy of the estimated state, and determines an uncertainty of the estimated state based on the estimated state and the accuracy of the estimated state.

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: The invention relates to a control system to which a state vector representing the states of a controlled system is applied. The control system provides a correcting variables vector of optimized correcting variables. The relation between state vector and correcting variables vector is defined by a matrix of weights. These weights depend on the solution of the state dependent Riccati equation. An equation solving system for solving the state dependent Riccati equation in real time is provided. The state vector is applied to this equation solving system. The solution of the state dependent Riccati equation is transferred to the control system to determine the weights.

Abstract: A system and method for evolving appropriate connections in feedforward topological networks. FIG. 1 illustrates an exemplary flow diagram of a method for evolving appropriate connections in a neural network device according to an aspect of the present invention. Initially, weight changes induced by each particular training sample or pattern are calculated using, for example, a conventional network training rule such as Hebb or backpropagation (step 101). Next, a ratio of the weight changes for existing connections to incipient connections (“K” ratio) is calculated (step 103). If this K ratio exceeds a specified threshold, weight changes are implemented (step 105), in which existing weights are increased by (1−E)×(total weight change of existing connections), where E is a network-wide parameter. The remaining amount (E)×(total weight change of existing connections) is added to form neighboring connections (step 107).

Abstract: A process and neural network architecture for on-line adjustment of the weights of the neural network in a manner that corrects errors made by a nonlinear controller designed based on a model for the dynamics of a process under control. A computer system is provided for controlling the dynamic output response signal of a nonlinear physical process, where the physical process is represented by a fixed model of the process. The computer system includes a controlled device for responding to the output response signal of the system. The computer system also includes a linear controller for providing a pseudo control signal that is based on the fixed model for the process and provides a second controller, connected to the linear controller, for receiving the pseudo control signal and for providing a modified pseudo control signal to correct for the errors made in modeling the nonlinearities in the process. A response network is also included as part of the computer system.

Abstract: An asymmetric data record processor and method includes host computers and job processing units (JPU's) coupled together on a network. Each host computer and JPU forms a node on the network. A plurality of software operators allow each node to process streams of records. For each operator in a given sequence within nodes and across nodes, output of the operator is input to a respective succeeding operator. Data processing follows a logical data flow based on readiness of a record. As soon as a record is ready it is passed for processing from one part to a next part in the logical data flow. The flow of records during data processing is substantially continuous and of a streaming fashion.

Abstract: Two neural networks are used to control adaptively a vibration and noise-producing plant. The first neural network, the emulator, models the complex, nonlinear output of the plant with respect to certain controls and stimuli applied to the plant. The second neural network, the controller, calculates a control signal which affects the vibration and noise producing characteristics of the plant. By using the emulator model to calculate the nonlinear plant gradient, the controller matrix coefficients can be adapted by backpropagation of the plant gradient to produce a control signal which results in the minimum vibration and noise possible, given the current operating characteristics of the plant.

Abstract: A class manager stores the information which is divided into hierarchical classes, wherein each class includes a policy for showing how to treat the information according to the security level, and information generating rules showing how to generate the information. A shared data space temporarily stores information. An instance generator generates new information based on the information generating rules and writes it in the shared data space. A security enforcement provides the information with controlling as complying with the policy relating to the information in the shared data space. A monitor monitors the writing and generation of the information in the shared data space.

Abstract: A method for determining a layer thickness distribution in a paint layer produced during paint spraying after inputting specific spraying parameters into an electrostatically based paint spraying device. A data processing device sets up and uses a phenomenological mathematical model of a quasi-stationary three-dimensional spray pattern. Specific parameters, such as an angle of rotation of electrodes and a rate of movement of the spraying device are input into the phenomenological model as fixed input parameters. In addition, real physical input parameters such as paint volume, directing air data and a voltage value, whose influence on the spraying result is not accurately known, are fed to an artificial neural network. The neural network having been previously trained using real input data such as a configuration of the spraying device, a paint type, operating parameters, and measured values of the layer thickness distribution.

Abstract: A computerized method determines the ranking of documents including information content. The present method uses both content and connectivity analysis. An input set of documents is represented as a neighborhood graph in a memory. In the graph, each node represents one document, and each directed edge connecting a pair of nodes represents a linkage between the pair of documents. The input set of documents represented in the graph is ranked according to the contents of the documents. A subset of documents is selected from the input set of documents if the content ranking of the selected documents is greater than a first predetermined threshold. Nodes representing any documents, other than the selected documents, are deleted from the graph. The selected subset of documents is ranked according the linkage of the documents, and an output set of documents exceeding a second predetermined threshold is selected for presentation to users.

Abstract: A method of predicting a carrying time in an automatic warehouse system. Input variables of a real automatic warehouse system are set in a virtual automatic warehouse system. The virtual automatic warehouse system is modeled based upon the set input variables. An optimal weighting value is set according to the modeling. A predicted carrying time is calculated based upon data input corresponding to the set variables and past history data related to a moving time(s) in the automatic warehouse system. An error between the predicted carrying time and a real carrying time in the automatic warehouse system is calculated. An optimal carrying path in the automatic warehouse system is selected according to the predicted carrying time if the error is within a preset allowable range.

Abstract: An integrated sensor processing cell device capable of transforming, reshaping, and modulating an original sensed image includes a sensing medium. At least one memory device stores weight bits. Multiplexers are associated with at least one of the memory devices. At least one transconductance amplifier is associated with at least one of the multiplexers. A multiple input logic gate, associated with at least one of the memory devices, is configured to store a signed pixel output derived from the sensing medium output.

Abstract: In a motor vehicle air conditioning installation comprising a central control unit (12), a computer network (14) connected to the central control unit (12) and a plurality of peripherals (101, 102, 103, . . . , 10n) connected to nodes (N1, N2, N3, . . . , Nn) of the computer network, the peripherals are connected in series via a common conductor (18). The installation comprises means (120) for inputting at the level of each peripheral an electric signature on a common conductor, means (130) for detecting an electric signal depending upon the electric signatures of the peripherals situated upstream on the common conductor (18), means for generating a data item representing the relative position of the peripheral on the basis of the electric signal detected, and means for identifying the node of the network to which it is connected on the basis of the relative position data item generated.

Abstract: A technique for estimating the best children (i.e. the next generation of individual chromosomes) for use by an online genetic analyzer (GA) is described. One embodiment provides a method for evaluation of a GA in a control system for controlling a plant. Individuals are evaluated using data for evaluation. The data for evaluation is categorized into subdomains of a larger evaluating domain. Data for evaluation is sorted into an evaluation domain wherein an evaluation value of each individual is replaced by a model of coefficients relative to a standard model. The standard model is used to calculate an evaluation value. The evaluation value can include a mean value and/or a distribution. Moreover, the mean value and distribution can be valid for an area including a periphery of the subdomain. The evaluation value can be calculated as a model obtained by using a least-squares method to find coefficients relative to coefficients of the standard model.

Abstract: A method and apparatus for color matching are provided, in which paint recipe neural networks are utilized. The color of a standard is expressed as color values. The neural network includes an input layer having nodes for receiving input data related to paint bases. Weighted connections connect to the nodes of the input layer and have coefficients for weighting the input data. An output layer having nodes are either directly or indirectly connected to the weighted connections and generates output data related to color values. The data to the input layer and the data from the output layer are interrelated through the neural network's nonlinear relationship. The paint color matching neural network can be used for, but not limited to, color formula correction, matching from scratch, effect pigment identification, selection of targets for color tools, searching existing formulas for the closest match, identification of formula mistakes, development of color tolerances and enhancing conversion routines.

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: Disclosed is an optimizing method for regulating the operating state of a guided machine tool having a tool rotating at a certain speed and subjected to a percussion frequency during a boring action, in which said tool, subjected in addition to a force, is driven into an object of a given material.

Abstract: A system of case-based reasoning for sensor prediction in a technical process, especially in a cement kiln, method and apparatus therefore, and wherein the system provides accurate predictions of the cement kiln behavior for a limited period into the future. The invention utilizes a method of case-base-reasoning (CBR) for the task of sensor value prediction. An apparatus for implementing the method is characterized by a database (100) in which the relevant time interval data are stored, a test generator (101) and an optimisation unit (102-104) for the selection and optimisation of the time interval data.

Abstract: A method is provided of adaptively controlling a paint system for painting vehicle bodies. The method includes the steps of obtaining an estimated Jacobian that identifies characteristics of the paint system and using the estimated Jacobian to determine initial conditions to be implemented for the next vehicle body to be painted in the paint system. The method also includes the steps of storing a database of appropriate initial conditions that resulted in satisfactory transient response in a rule-base and using the rule-base at start-up of the paint system or if the paint system fails to be satisfactorily controlled.

Abstract: A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

Abstract: The present invention is directed to a system for managing business knowledge expressed as statements, preferably sentences using a vocabulary, where such statements may be automated by the generation of programming language source code or computer program instructions. As such, the present invention also manages software design specifications that define, describe, or constrain the programming code it generates or programs with which it or the code it generates is to integrate. All information managed within the present invention is maintained within a relational database that is encapsulated within an object-oriented model. Each object in this model is subject to version control and administration using permissions. Each user of the system is an object and belongs to one or more groups. Users and groups may be granted privileges. Objects may be created, examined, used, modified, deleted, or otherwise operated upon only if corresponding permission or privilege has been granted.

Abstract: Optimization control of a control system by improving evolution efficiency while retaining the advantages of genetic optimization is described. An optimizer for evolving control parameters affecting the characteristics of a control system uses actual user fitness evaluations of various control parameters or control parameters arranged as chromosomes. Parameters or chromosomes for a next generation are pre-processed using an evaluation model that estimates the fitness of new chromosomes before the chromosomes are presented to the user. Chromosomes that have a low estimated fitness value are modified or deleted, thereby reducing the number of low-quality chromosomes evaluated by the user.

Abstract: A Robust Model-Free Adaptive controller is disclosed for effectively controlling simple to complex systems including industrial processes, equipment, facilities, devices, engines, robots, vehicles, and appliances. Without the need of re-designing a controller or re-tuning the controller parameters, the inventive controller is able to provide a wide robust range and keep the system under automatic control during normal and extreme operating conditions when there are significant disturbances or changes in system dynamics. Because of its simplicity and capability, the control system is useful for building flexible and adaptive production systems to fulfill the on demand manufacturing needs of the new e-commerce environment.

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 vacuum network control system includes a vacuum network controller hub communicating over a public high-speed network, where the hub has an address registered with respect to the non-local network and communicates over a first local high-speed network. Vacuum network controllers (VNCs) communicate with the hub over the first local network. Each VNC has a dynamically assigned local address and communicates with at least one component intrabus module over a second local high-speed network. Each module has a dynamically assigned local address and further communicates with one or more monitor and control end units over a local device network. Each monitor and control end unit has a dynamically assigned address. Monitor and control end units include taps, which provide at least one of analog, digital or serial I/O, and components, for example, vacuum pumps.

Abstract: It is objective of the invention to provide a highly reliable control apparatus and method which reduces the amount of calculation required for both learning of an input/output relationship and actual control as well as prevent inappropriate outputs from being generated for inputs which have been never learned. According to one aspect of the invention, pairs of an input pattern vector for learning and a target output are distributed to a class based on the target output. Then, a correspondence between each element of the input pattern vector for learning and the target output is learned only in that class, and a distribution function is calculated for distributing a new input pattern vector for learning to that class. After the completion of this learning, the distribution function is used to determine which class a new input pattern vector detected by a sensor belongs to. Finally, an output is calculated according to the learning result of that class.

Abstract: A distributed system provides for separate management of dynamic cluster membership and distributed data. Nodes of the distributed system may include a state manager and a topology manager. A state manager handles data access from the cluster. A topology manager handles changes to the dynamic cluster topology. The topology manager enables operation of the state manager by handling topology changes, such as new nodes to join the cluster and node members to exit the cluster. A topology manager may follow a static topology description when handling cluster topology changes. Data replication and recovery functions may be implemented, for example to provide high availability.

Abstract: Because of the enormous number of neurons and reflex pathways involved in the control of human motion, a comprehensive identification of the structure of the feedback control circuitry is a formidable experimental task. A theoretical approach based on a carefully developed model could shed light on its likely general structure, thereby providing guidance to experimenters. This paper presents a fuzzy logic based control strategy for modeling the human postural dynamics. In particular, we develop a fuzzy controller to coordinate the movement of a three segment sagittal human model to achieve upright posture. The actuation mechanism of the system consists of six muscle like actuators which are co-activated to modulate the stiffness at the joints. Simulation results are presented to illustrate the underlying principles.

Abstract: A robust Artificial Neural Network controller is proposed for the motion control of a magnetic disk drive voice coil motor (voice coil motor). The neural controller is used to approximate the nonlinear functions (actuator electromechanical dynamics) of the voice coil motor while having on line training. One main advantage of this approach, when compared with standard adaptive control, is that complex dynamical analysis is not needed. Using this design, not only strong robustness with respect to uncertain dynamics and non-linearities can be obtained, but also the output tracking error between the plant output and the desired reference can asymptotically converge to zero. Additionally, standard offline training, utilizing training vectors to stimulate the voice coil motor, is not required.

Abstract: The invention includes an adaptive control system used to control a plant. The adaptive control system includes a hedge unit that receives at least one control signal and a plant state signal. The hedge unit generates a hedge signal based on the control signal, the plant state signal, and a hedge model including a first model having one or more characteristics to which the adaptive control system is not to adapt, and a second model not having the characteristic(s) to which the adaptive control system is not to adapt. The hedge signal is used in the adaptive control system to remove the effect of the characteristic from a signal supplied to an adaptation law unit of the adaptive control system so that the adaptive control system does not adapt to the characteristic in controlling the plant.

Abstract: Methods and systems for backlash compensation. Restrictive assumptions on the backlash nonlinearity (e.g. the same slopes of the lines, etc.) are not required. The compensator scheme has dynamic inversion structure, with a neural network in the feedforward path that approximates the backlash inversion error plus filter dynamics needed for backstepping design. The neural network controller does not require preliminary off-line training. Neural network tuning is based on a modified Hebbian tuning law, which requires less computation than backpropagation. The backstepping controller uses a practical filtered derivative, unlike the usual differentiation required by earlier backstepping routines. Rigorous stability proofs are given using Lyapunov theory. Simulation results show that the proposed compensation scheme is an efficient way of improving the tracking performance of a vast array of nonlinear systems with backlash.

Abstract: To provide a system for providing information that allows operators who orally answer questions about certain products from users to deal with the users quickly and effectively. The level of information disclosure is specified for each electronic information to be used for customer support in an information server. In response to the reception of a request for information from an operator terminal, knowledge levels of the user and the operator are determined. The associated electronic information is edited according to rules of providing information that are determined according to the specified knowledge level. The edited version of the electronic information is supplied to the operator terminal. The extent to which information will be disclosed varies according to the level of the knowledge of the user or the operator for a certain product.

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

Abstract: An intelligent control method for injection machine is to transplant the intelligent control and prediction techniques of a neural network to an injection machine, which has been exemplified capable of deciding the quasi best machine parameters rapidly in couple processing cycles for increasing yield with least loss, and for detecting and adjusting conditions until a desired operation environment is obtained.

Abstract: A method and apparatus for using a neural network to process information includes multiple nodes arrayed in multiple layers for transforming input arrays from prior layers or the environment into output arrays for subsequent layers or output devices. Learning rules based on reinforcement are applied. Interconnections between nodes are provided in a manner whereby the number and structure of the interconnections are self-adjusted by the learning rules during learning. At least one of the layers is used as a processing layer, and multiple lateral inputs to each node of each processing layer are used to retrieve information. The invention provides rapid, unsupervised processing of complex data sets, such as imagery or continuous human speech, and captures successful processing or pattern classification constellations for implementation in other networks.

Abstract: A method and a device for controlling a process (10), having at least two sub-processes (20, 30), by tracking, processing and correcting variables for the product, the production means and/or any process media throughout the process line. The process flow for the first sub-process (20) is divided into slices, each slice representing a specific volume of process flow. At least some of the measured and sampled variable values are related to its specific slice volume.

Abstract: A system for consolidating and sorting event data include a computing platform communicatively coupled to a computer readable medium and a network. The computer readable medium may store an application that includes at least one node mapped into a tree. The at least one node may have a data element reference including a pointer to a data element that includes event data received via the network. In addition, the node may have a row indicator node count, a least child reference, a greatest child reference, a lesser sibling reference, a greatest sibling reference, a parent reference, and a status manager reference.

Abstract: A modular optimizer, for use in determining a configuration of a production line with one or more component placement machines, is configured to learn a foreign optimizer module associated with a component placement machine type foreign to the modular optimizer. The modular optimizer includes an adapting estimator which estimates an output value, such as a placement cycle time measure, for the foreign optimizer module. The estimated output value may be used in determining the configuration of the production line.

Abstract: Systems and methods for autonomously controlling agricultural machinery such as a grain combine. The operation components of a combine that function to harvest the grain have characteristics that are measured by sensors. For example, the combine speed, the fan speed, and the like can be measured. An important sensor is the grain loss sensor, which may be used to quantify the amount of grain expelled out of the combine. The grain loss sensor utilizes the fluorescence properties of the grain kernels and the plant residue to identify when the expelled plant material contains grain kernels. The sensor data, in combination with historical and current data stored in a database, is used to identify optimum operating conditions that will result in increased crop yield. After the optimum operating conditions are identified, an on-board computer can generate control signals that will adjust the operation of the components identified in the optimum operating conditions.

Abstract: A computing machine capable of performing multiple operations using a universal computing unit is provided. The universal computing unit maps an input signal to an output signal. The mapping is initiated using an instruction that includes the input signal, a weight matrix, and an activation function. Using the instruction, the universal computing unit may perform multiple operations using the same hardware configuration. The computation that is performed by the universal computing unit is determined by the weight matrix and activation function used. Accordingly, the universal computing unit does not require any programming to perform a type of computing operation because the type of operation is determined by the parameters of the instruction, specifically, the weight matrix and the activation function.

Abstract: A method for modeling a paper winding device, particularly for modeling a tambour drum cutter. Influencing and control quantities are determined at a real paper winding device and are stored dependent on time. Said quantities are used to determine the web strength depending on the wound number of layers or, respectively, or a correlative quantity is used as a target quantity along with the other relevant control and influencing quantities to enable a neural network to be trained as a model for a nip (Ni) for this winding device. New data for training the network can be continually obtained during the operation of the arrangement, thereby improving the model. Optimal control parameters can be determined for various production requirements by means of an iterative process dependent on the winding quality, which can also be determined from the web strength.

Abstract: Provided is an appliance control system and method for remotely controlling various types of electrical home appliances using a mobile communications terminal, and a home gateway. To operate the electrical home appliances which vary in manufacturer and type, a database 71 in a database center 7 stores each corresponding appliance control program. In a household area 3, the electrical home appliance(s) 4 are connected to a home gateway 6 via a household network 5. The home gateway 6 collectively controls information about the storage locations of the appliance control programs on the database 71 together with various types of information about the electrical home appliances 4. A mobile communications terminal 2 refers to the home gateway 6 so as to acquire from the database 71 only the appliance control program of any electrical home appliance 4 which a user desires to operate.

Abstract: A method and an electronic circuit arrangement is provided which allows observation of bus-systems and networks. In particular the method and circuit arrangement does not cause electrical or physical constraints and influence to the bus-systems and networks. Still more specifically it provides for accessing static bus-systems and networks in particular used by real-time constrained systems and applications such as in automotive vehicles.

Abstract: Generally, an Eigen network and system using same are disclosed that use Principal Component Analysis (PCA) in a middle (or “hidden”) layer of a neural network. The PCA essentially takes the place of a Radial Basis Function hidden layer. A classifier comprises inputs that are routed to a PCA device. The PCA device performs PCA on the inputs and produces outputs (entitled “PCA outputs” for clarity). The PCA outputs are connected to output nodes. Generally, each output is connected to each output node. Each connection is multiplied by a weight, and each output node uses weighted PCA outputs to produce an output (entitled a “node output” for clarity). These node outputs are then generally compared in order to assign a class to the input. A system uses the PCA classifier to classify input patterns. In a third aspect of the invention, a PCA classifier is trained in order to determine weights for each of the connections that are connected to the output nodes.

Abstract: A method, apparatus, and computer software are provided whereby to associate a readily user-interpretable reason with an output of a supervised training data classifier. Reasons are associated with one or more members of a sequence of training vectors and subsequently associated, in operation, with a given classifier input by comparing the classifier input vector with training sequence vectors. A measure of confidence in the selected reasons is derived by comparing the classifier input vector with the corresponding training inputs with which the selected reasons were associated and calculating a measure of their closeness.

Abstract: This invention includes the generation of forming information and its manipulation scheme as a method to form curved plates in ship hull-pieces. This invention consists of three components as follows: one is to construct and utilize a database which includes data about flat plates, objective curved plates, plates which are being formed, and their forming information, another is to infer new forming information with an artificial neural network system, and the third is to obtain forming information through calculating in-plane and bending strains. In the third, initial forming information is obtained by calculating strains from relationship between flat plates and objective curved plates. And new forming information is yielded through calculating the strains from relationship between partially formed curved plates and objective curved plates.

Abstract: An elevator operation management and control system includes a traffic data collecting section for collecting traffic data for finding a traffic amount, a traffic amount calculating section for calculating the traffic amount based on the traffic data collected by the traffic data collecting section, a traffic flow calculating section for calculating an estimated traffic flow of the elevator users moving between floors based on the traffic amount calculated by the traffic amount calculating section, a control parameter setting section for setting control parameters for controlling operations of the elevators based on the estimated traffic flow calculated by the traffic flow calculating section and an operation control section for controlling the operations of the elevators based on the control parameters set by the control parameter setting section, eliminating the need for preparing and storing combinations of a large number of traffic flow patterns and traffic amounts obtained from the traffic flow patterns

Abstract: A plumbing control system includes a plurality of plumbing fixtures operated by an electronic control board having a microprocessor. The control board can form a node on a network that monitors and controls the functions of multiple boards throughout a facility from a central computer. Network software running on the central computer communicates with the control boards. The software includes a binding feature that requires reporting of a network operating variable at a first control board to the central computer or a second control board. The network software also has site and device setup forms.

Abstract: A process device couples to a process control loop. The process device receives process signals. A memory in the process device contains a nominal parameter value and a rule. Computing circuitry calculates a statistical parameter of the process signal and operates on the statistical parameter and the stored nominal value based upon the stored rule and responsively provides an event output based upon the operation. Output circuitry provides an output in response to the event output. The statistical parameter can be used in statistical process control systems.

Abstract: A technique for machine learning, such as supervised artificial neural network learning includes receiving data and checking the dimensionality of the read data and reducing the dimensionality to enhance machine learning performance using Principal Component Analysis methodology. The technique further includes specifying the neural network architecture and initializing weights to establish a connection between read data including the reduced dimensionality and the predicted values. The technique also includes performing supervised machine learning using the specified neural network architecture, initialized weights, and the read data including the reduced dimensionality to predict values. Predicted values are then compared to a normalized system error threshold value and the initialized weights are revised based on the outcome of the comparison to generate a learnt neural network having a reduced error in weight space.