Abstract: A face recognition method using artificial neural network and an apparatus thereof are provided. The apparatus comprises an eigenpaxel selection unit which generates eigenpaxels indicating characteristic patterns of a face and selects a predetermined number of eigenpaxels among the generated eigenpaxels; an eigenfiltering unit which filters an input image with the selected eigenpaxels; a predetermined number of neural networks, each of which corresponds to one of the selected eigenpaxels, receives an image signal which is filtered by the corresponding eigenpaxel, and output a face recognition result; and a determination unit which receives the recognition result from each of the neural networks and outputs a final face recognition result of the input image.

Abstract: The present invention relates to a system of artificial intelligence for the classification of events, objects or situations from signals and discriminant parameters produced by models comprising at least one processing branch comprising a fuzzy expert system making a decision according to high level properties and discriminant parameters of low level extracted from signals by signal processing type procedures, and capable of explaining its decision to the user through the intermediary of rules selected by order of applicability.

Abstract: One embodiment of the present invention provides a system that migrates a layout of a cell which is used in integrated circuit design. During operation, the system receives a layout for the cell, wherein one or more layers of the cell contain tracks for metal wires. The system then determines how many tracks are to be inserted into the cell. Next, the system inserts one or more extra tracks between the tracks in the cell. The system subsequently adjusts the widths of the inserted tracks and the original tracks to increase the total number of tracks within the cell while maintaining the metal wires at the center of their original tracks.

Abstract: A handwriting recognition device using fuzzy logic and cellular neural network for unconstrained handwritten numeral classification is provided. The current mode VLSI classifier has a I/O circuit for inputting and outputting a plurality of membership functions. An extraction unit comprising a CCD extractor with a CNN structure and a compression unit receives a to-be-recognized character having a plurality of input features for generating a plurality of features values after compression. A membership function generator stores the plurality of membership functions and receives the plurality of features values to generate a plurality of current-type membership degrees. A plurality of switched-current integrators receives the plurality of current-type membership degrees for generating a plurality of synthesis membership degrees.

Abstract: A neural fuzzy data classifier for handwritten character classification is provided. The classifier has an I/O circuit for inputting and outputting a plurality of membership functions. An input buffer receives a to-be-recognized character having a plurality of input features for generating a plurality of features values. A main computation unit comprising a computation array and a local thresholds block store the plurality of membership functions and receives the plurality of features values to generate a plurality of current-type weights. A difference circuit block compares the current output from the computation array and local thresholds lock. A plurality of switched-current integrators receives the plurality of current-type weights for generating a plurality of synthesis weights.

Abstract: A method and system for determining the state-of-health of a nickel-metal hydride battery connected to a load comprises: detecting at least one of a real and imaginary part of an impedance of the battery at each of a first set of selected frequencies, the first set of selected frequencies including at least one frequency, and determining the state-of-health of the battery from a fuzzy system trained in a relationship between each impedance and the state-of-health, wherein the state-of-health is a function of the battery's ability to deliver power required by the load and the battery's capacity to meet load requirements.

Abstract: A method for generating a network, in particular a telecommunications, water, long-distance heat supply, or power network, the network connecting all users to a main distribution node depending on the existing or definable local needs and requirements of the individual users. A graph is generated which is composed of edges and nodes. The graph includes all technically feasible and/or definable transmission paths of the network. The length and direction of the edges are derived from the real topography of the street segments and definable cable paths of the territory to be supplied by the network. The nodes form the intersections between the edges or streets and/or cable paths. The users are assigned to the graph in such a way that each user is connected to the closest edge or the closest node of the graph by an additional service edge.

Abstract: The expression data of specimen genes are processed using the SWEEP operator method and the parameter increasing method, and genes are selected. A FNN model is constructed by making the expression data of the selected genes as input variables.

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: A system is controlled by an actor-critic based fuzzy reinforcement learning algorithm that provides instructions to a processor of the system for applying actor-critic based fuzzy reinforcement learning. The system includes a database of fuzzy-logic rules for mapping input data to output commands for modifying a system state, and a reinforcement learning algorithm for updating the fuzzy-logic rules database based on effects on the system state of the output commands mapped from the input data. The reinforcement learning algorithm is configured to converge at least one parameter of the system state to at least approximately an optimum value following multiple mapping and updating iterations. The reinforcement learning algorithm may be based on an update equation including a derivative with respect to at least one parameter of a logarithm of a probability function for taking a selected action when a selected state is encountered.

Abstract: Machine operators rely on intuition and experience to evaluate vehicle performance. As we increasingly turn to automation, it is important to automatically evaluate sensor data and system performance. Fuzzy logic allows us to take advantage of domain knowledge to evaluate data and to describe a system linguistically. In this application a fuzzy quality module evaluates output from a noisy sensor. It compares the sensor data with an estimated sensor value, and based on that comparison, dynamically adapts a fuzzy logic rulebase used to determine whether the sensor value is valid or not.

Abstract: A system for tuning a process control loop includes a tuner module for receiving an error signal representative of the difference between a set point and a process variable, the module generating a first process control signal for controlling the process. The system further includes a controller module for receiving the error signal and a parameter signal from a nonlinear module to generate a second process control signal for controlling the process, wherein the nonlinear module applies a nonlinear procedure to generate the parameter signal. The system further includes a switching means coupled to the tuner module and the controller module to select the appropriate process control signal for controlling the process. The system provided uses nonlinear techniques in the nonlinear module to approximate the desired controller tuning parameters. The nonlinear techniques include neural network tuning, fuzzy logic tuning and nonlinear functions, including sigmoid tuning.

Abstract: A method is provided for deriving a near-optimal fuzzy automaton for a given separation problem. The method includes the steps of: forming a first generation population (24) of fuzzy automata, where the first generation population of fuzzy automata includes a plurality of fuzzy automata; performing a mutation operation (28) on each fuzzy automaton in the first generation population of fuzzy automata; reproducing the first generation population of fuzzy automata using a survival of the fittest operation (30, 32, 34); and applying a cross-over operator (36) to the reproduced first generation population of fuzzy automata, thereby yielding a next-generation population of fuzzy automata. A near-optimal fuzzy automaton is identified by evaluating the performance (38) of each fuzzy automaton in the next-generation population; otherwise the methodology is repeated until a near-optimal fuzzy automaton is derived for the given separation problem.

Abstract: An apparatus and technique for Digital Subscriber Line (DSL) telephone loop qualification that includes a fuzzy logic impedance mismatch network. The impedance mismatch network is used to increase the received echo of a transmit pulse signal to determine the telephone loop characteristics.

Abstract: Latent effects on a system are broken down into components ranging from those far removed in time from the system under study (latent) to those which closely effect changes in the system. Each component is provided with weighted inputs either by a user or from outputs of other components. A non-linear mathematical process known as ‘soft aggregation’ is performed on the inputs to each component to provide information relating to the component. This information is combined in decreasing order of latency to the system to provide a quantifiable measure of an attribute of a system (e.g., safety) or to test hypotheses (e.g., for forensic deduction or decisions about various system design options).

Abstract: Control apparatus for a horizontal boring tool is described which drives a boring lance via boring rods. The tool includes an input interface for receiving actual values of controlled variables of the horizontal boring tool, and an output unit for issuing control signals for controlling the horizontal boring tool. Between the input interface and the output unit is provided a fuzzy control unit which determines the control signals for activation of the horizontal boring tool from the actual values of the controlled variables and the desired values for the controlled variables by fuzzy logic while taking into account heuristic process values. The control apparatus permits automatic operation of the horizontal boring tool with good course steering and high precision.

Abstract: Methods and systems for normalizing flood warnings and potential flood levels across multiple river gauges by converting localized flood stage levels to a datum-based normalized value are disclosed. The normalized flood levels can then be used to plot flood levels from multiple river gauges on an inundation map. Methods and systems are also disclosed for creating animated inundation maps that illustrate flood levels geographically, and may include an indication of the certainty that flooding will reach one or more of the indicated flood levels through the use of multiple colors, shades, dithering, or the like.

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: The AI System for protein superfamily classification is related to an artificial intelligence system for protein family classification using the fuzzy inference theory in a neural network to improve robustness, convergence and correctness. In addition, the system uses a content addressable memory to process the early phase of the classification to improve the execution speed.

Abstract: A fuzzy-neuro method uses a neuro network to discriminate optical disk type. The fuzzy-neuro method establishes a plurality of characteristic signal sets corresponding to the optical disks of various formats; and uses the neuro network to match the optical disk under question to the characteristic signal sets for finding the type thereof.

Abstract: Real-time optimization is conducted for a control device having a fuzzy inference system that can optimize characteristics of the control device in real time, by coding parameters used in the fuzzy inference system into chromosomes, and optimizing in real time the output of the control device by subjecting the chromosomes to evolutionary computation. Coding is conducted by (i) revising a matrix of fuzzy rules used in the system by extracting a section from the matrix and coding elements of the section into chromosomes; (ii) modifying the configuration of the fuzzy rule matrix defined by membership functions by coding elements of the membership functions into chromosomes; or (iii) changing a level of an input of the parameters and a level of an output of the fuzzy inference system.

Abstract: A reduced control system suitable for control of a nonlinear or unstable plant is described. The reduced control system is configured to use a reduced sensor set for controlling the plant without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

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 is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

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: A method of and system for determining the state-of-health of a proton exchange membrane fuel cell stack connected to a load comprises detecting the real part of the impedance Z1 of the fuel cell stack at a selected frequency; detecting the voltage V1 of the fuel cell stack at open circuit; detecting the voltage V2 of the fuel cell stack when the maximum load current is being drawn from said fuel cell stack; and determining the state of health of said fuel cell stack from a fuzzy system trained in a relationship between said real part of the impedance of the fuel cell stack at the selected frequency, the voltage of the fuel cell stack at open circuit, and the voltage of the fuel cell stack when the maximum load current is being drawn from said fuel cell stack.

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 handwriting recognition device using fuzzy logic and cellular neural network for unconstrained handwritten numeral classification is provided. The current mode VLSI classifier has a I/O circuit for inputting and outputting a plurality of membership functions. An extraction unit comprising a CCD extractor with a CNN structure and a compression unit receives a to-be-recognized character having a plurality of input features for generating a plurality of features values after compression. A membership function generator stores the plurality of membership functions and receives the plurality of features values to generate a plurality of current-type membership degrees. A plurality of switched-current integrators receives the plurality of current-type membership degrees for generating a plurality of synthesis membership degrees.

Abstract: A method of and system for determining the state-of-health of a lead acid battery of the type commonly used in portable external defibrillators comprises detecting an impedance characteristic of the battery at at least one selected frequency, counting the number of complete charge/discharge cycles that the battery undergoes, and determining the state of health of the battery from a fuzzy system trained in a relationship between the impedance characteristic and the cycle number of the lead acid battery and the state-of-health, wherein the state-of-health is a function of and varies with the battery's ability to deliver power required by the load and the battery's capacity to meet load requirements.

Abstract: A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.

Abstract: The invention relates to a method for automatic interpretation of geochemical measurements obtained by pyrolysis of a rock sample in order to obtain information pertaining to the organic matter contained in the sample. In this method, rock samples having known petroleum characteristics are used to carry out a phase of training of an artificial neural network, the neural network is used to obtain parameters pertaining to the organic matter of a rock sample, interpretation of the parameters is refined at the network output by using fuzzy sets for refining interpretation of the parameters at an output of the neural network.

Abstract: A method of training a neural network for the guidance of a missile to a target includes the steps of: computing a solution of the non-linear guidance problem in analytical form, generating numerical solutions for a number of flights of a virtual missile to a virtual target, determining a human pilot's behavior and of the missile by simulating a number of flights of the virtual missile to a virtual target, and “cloning” a neural or fuzzy-neural network with the knowledge about the guidance of the missile to the target as obtained by the preceding steps. For determining the behavior of the human pilot, a scenario of missile and target is represented. This scenario is transformed into slow-motion. The flight of the missile to the target is simulated in slow-motion, the human pilot guiding the missile to the target. The pilot's behavior and the behavior of the missile resulting therefrom is stored for a number of such simulated flights.

Abstract: A method and apparatus for providing learning capability to processing device, such as a computer game, educational toy, telephone, or television remote control, is provided to achieve one or more objective. For example, if the processing device is a computer game, the objective may be to match the skill level of the game with that of a player. If the processing device is an educational toy, the objective may be to increase the educational level of a user. If the processing device is a telephone, the objective may be to anticipate the phone numbers that a phone user will call. If the processing device is a television remote control, the objective may be to anticipate the television channels that will watched by the user. One of a plurality of actions (e.g., game actions, educational prompts, listed phone numbers, or listed television channels) to be performed on the processing device is selected. A user input indicative of a user action (e.g.

Abstract: The invention provides a neural network module comprising an input layer comprising one or more input nodes arranged to receive input data, a rule base layer comprising one or more rule nodes, an output layer comprising one or more output nodes, and an adaptive component arranged to aggregate selected two or more rule nodes in the rule base layer based on the input data. The invention also provides an adaptive learning system comprising one or more of the neural network modules of the invention. The invention further provides related methods of implementing a neural network module an adaptive learning system, and a neural network computer program.

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 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 control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and/or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. An information filter is used to filter the teaching signal to produce a compressed teaching signal. The compressed teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. In one embodiment, the control system includes a learning system, such as a neural network that is trained by the compressed training signal. The learning system is used to create a knowledge base for use by an online fuzzy controller. The online fuzzy controller is used to program a linear controller.

Abstract: A system and a method for diagnosis of engine conditions are proposed. In particular, the system and the method are directed to an extraction of features from different information sources and to their processing. These features, together with a series connection of two neural networks, form the crux of the system and method, so that a dependable diagnosis of engine conditions, particularly an error recognition is possible. As a result thereof, maintenance corresponding to the current engine condition is enabled.

Abstract: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior.

Abstract: In one form of the invention, a computer database storage system is disclosed, comprising a data storage medium adapted to store a plurality of pieces of information, at least one piece of data stored in the data storage medium, and at least one rule stored in the data storage medium, each said at least one rule comprising a premise, an action, wherein the action is performed if the premise is determined to be true, an alternate action, wherein the alternate action is performed if the premise is determined to be false, and a trigger, wherein the trigger causes evaluation of the premise upon the occurrence of a predetermined event.

Abstract: Variation in results of a semiconductor fabrication process is minimized by adjusting process parameters utilizing a fuzzy-controlled learning feedback system. The fuzzy-controlled learning system receives as inputs the results of the fabrication process and then converts these results into a fuzzy set defined by a membership function. An inference engine applies a fuzzy rule base to map an output fuzzy set from the input fuzzy set. The fuzzy output set is then converted to crisp outputs which automatically adjusts parameters of the fabrication process in order to minimize variation.

Abstract: A control system for a harvester or similar implement includes a supervisory controller, a set of low-level controllers and a neuro-fuzzy inference system. The supervisory controller employs human expert knowledge and fuzzy logic. The controller monitors the quality of the harvesting process, such as gain loss, dockage, grain damage and the like. Based on the measurements, setpoints for all critical functional elements of the implement are determined. The neuro-fuzzy inference system determines machine settings according to operating conditions and learns from harvester experience. The parameters of the neuro-fuzzy inference system are stored in on-board memory. The neuro-fuzzy system can be used for harvester set-up and as one of the knowledge sources for repeated adjustments during the harvest.

Abstract: A method and system for determining the state-of-health of a nickel-metal hydride battery connected to a load comprises: detecting at least one of a real and imaginary part of an impedance of the battery at each of a first set of selected frequencies, the first set of selected frequencies including at least one frequency, and determining the state-of-health of the battery from a fuzzy system trained in a relationship between each impedance and the state-of-health, wherein the state-of-health is a function of the battery's ability to deliver power required by the load and the battery's capacity to meet load requirements.

Abstract: A method of and system for determining the state-of-health of a proton exchange membrane fuel cell stack connected to a load comprises detecting the real part of the impedance Z1 of the fuel cell stack at a selected frequency; detecting the voltage V1 of the fuel cell stack at open circuit; detecting the voltage V2 of the fuel cell stack when the maximum load current is being drawn from said fuel cell stack; and determining the state of health of said fuel cell stack from a fuzzy system trained in a relationship between said real part of the impedance of the fuel cell stack at the selected frequency, the voltage of the fuel cell stack at open circuit, and the voltage of the fuel cell stack when the maximum load current is being drawn from said fuel cell stack.

Abstract: A method of and system for determining the state-of-health of a lead acid battery of the type commonly used in portable external defibrillators comprises detecting an impedance characteristic of the battery at at least one selected frequency, counting the number of complete charge/discharge cycles that the battery undergoes, and determining the state of health of the battery from a fuzzy system trained in a relationship between the impedance characteristic and the cycle number of the lead acid battery and the state-of-health, wherein the state-of-health is a function of and varies with the battery's ability to deliver power required by the load and the battery's capacity to meet load requirements.

Abstract: An image production method has steps of: a detecting one or more descriptive regions exclusive of a background portion of a manuscript; calculating an image characteristic value of said descriptive region; selecting one from a plurality of previously defined layout patterns based on said determined image characteristic value, the layout pattern including information at least about a position of the descriptive region; and producing an image by laying out an image of said manuscript descriptive region or images of an illustration and said manuscript descriptive region according to the decided layout pattern.

Abstract: This invention proposes a novel nonlinear filter called adaptive weighted fuzzy mean (AWFM) filter which is capable of removing high density Gaussian impulse noises in polluted images. Via a single epoch, weights of the filter are departed according to a set of fuzzy rules, which are constructed by a proposed procedure. All the fuzzy rules in the knowledge base infer concurrently for input nature estimation and filter adaptation. Statistic analyses ensure that this AWFM filtering possesses robust and consistent performance even when images are thoroughly polluted. The AWFM filter is synthesized with generic LR fuzzy cells which adopts CMOS analog current mode technology to subject high speed, low power, and small circuit area objectives. Simulations show that parallel inferences realized by the generic LR fuzzy cells, each of which performs 20 M FLIPS (fuzzy logic inferences per second) promise that the synthesized AWFM filter is able to process up to 50 256×256 images per second.

Abstract: A fuzzy-neuro method uses a neuro network to discriminate optical disk type. The fuzzy-neuro method establishes a plurality of characteristic signal sets corresponding to the optical disks of various formats; and uses the neuro network to match the optical disk under question to the characteristic signal sets for finding the type thereof.

Abstract: The present invention relates to a data processing system in a hierarchical network configuration for executing applicable data processes in a comprehensible and executable form. An object of the present invention is to allow data processing capabilities to be established with high precision in a short time based on a fuzzy-neuro-integrated concept. A fuzzy model is generated by a data processing system in the form of membership functions and fuzzy rules as technical information relating to a control target. According to this fuzzy model, a weight value of the connection between neurons is set and a pre-wired neural network is established. Then, the data of the control target are learned by the neural network. The connection state and a weight value of the neural network after the learning enable tuning of the fuzzy model.

Abstract: A method for determining state of health (SOH) of an electrochemical device using fuzzy logic (i.e., an intelligent system) is presented. State of health of an electrochemical device is determined by an internal characteristic parameter (or external operating and environmental conditions) of the electrochemical device and a characteristic parameter of a load with an intelligent system. The electrochemical device comprises such devices as primary (“throwaway”) batteries, rechargeable batteries, fuel cells, hybrid batteries containing a fuel cell electrode or electrochemical supercapacitors. The intelligent system is trained in the relationship between the characteristic parameters of the electrochemical device, the characteristic parameters of the load and the SOH of the electrochemical device.