Abstract: In a method for detecting the modes of a dynamic system with a large number of modes that each have a set ? (t) of characteristic system parameters, a time series of at least one system variable x(t) is subjected to modeling, for example switch segmentation, so that in each time segment of a predetermined minimum length a predetermined prediction model, for example a neural network, for a system mode is detected for each system variable x(t), whereby modeling of the time series is followed by drift segmentation in which, in each time segment in which there is transition of the system from a first system mode to a second system mode, a series of mixed prediction models is detected produced by linear, paired superimposition of the prediction models of the two system modes.

Abstract: A device for calculating numerical solutions for partial differential equations in successive intervals using adaptive meshes, comprises: a neural network part for producing predictions of gradients at a following interval based on gradients available from previous intervals, and a mesh adaptation part, associated with the neural network part, configured for adapting a mesh over a domain of a respective partial differential equation using the predictions, such that the mesh adaptively refines itself about emerging regions of complexity as the partial differential equation progresses over the successive intervals. The neural network part succeeds in its predictions since its use herein is equivalent to using time series function fitting techniques.

Abstract: Methods and systems for obtaining the performance characteristics of a computing product are described. Obtaining a computing product's attributes, capabilities, and features includes assessing the computing product to determine the product's attributes, capabilities, and features. Once the assessment is completed, the assessment data is recorded and stored for future applications. The assessments can be performed by the operating system through an assessment tool. Assessments can be performed on various computing products including personal computers, computer components, clusters of computers, and servers.

Abstract: A database management system provides the capability to perform cluster analysis and provides improved performance in model building and data mining, good integration with the various databases throughout the enterprise, and flexible specification and adjustment of the models being built, but which provides data mining functionality that is accessible to users having limited data mining expertise and which provides reductions in development times and costs for data mining projects. The database management system for in-database clustering comprises a first data table and a second data table, each data table including a plurality of rows of data, means for building an Orthogonal Partitioning Clustering model using the first data table, and means for applying the Orthogonal Partitioning Clustering model using the second data table to generate apply output data.

Abstract: A process, system and computer software are provided to produce at least two solutions related to a knowledge discovery from data. In particular, the information regarding operators which are usable for the knowledge discovery of the data is received, and the solutions are generated. Each of the solutions includes at least one of the operators. An ability is provided to select at least one of the solutions so as to execute one or more procedures from the data. Each of the procedures is associated with the operator of the respective solution. In addition, it is possible to include a variable number of the operators in at least one (and possibly all) of the solutions. Also, it is possible to generate a code for at least one automatically-generated solution. In particular, access to the automatically-generated solution can be obtained with this solution that includes operators usable for the knowledge discovery of the data.

Abstract: A computer numerical control unit with learning ability solves the problem of automatic and intelligent generating of numerical control programs for computer numerical control machining centers for milling, drilling and similar operations. The key module of the computer numerical control unit is a neural network (NN) device that learns to generate the numerical control programs through an neural network teaching module. Upon completion of learning process the neural network device can generate automatically, without any intervention of the operator, merely on the basis of the CAD 2D, 2,5D or 3D part models, taken from a conventional CAD/CAM system, various new numerical control programs for different parts, which have not been in the machining process before.

Abstract: The profitability and effectiveness of a marketing program is increased by segmenting the customer population according to a combination of different segmentation strategies. A number of independent segmentation strategies are performed on the customers, each strategy resulting in its own set of scores. The sets of scores are then combined to form a composite score for each customer which is used to generate a ranked list of the customer population. Furthermore, different composite scores can be determined using different possible methods and these different scores can, themselves, be combined to generate an overall score and ranking for each customer. The targeted recipients for particular marketing materials are selected based on these rankings.

Abstract: A distributed OLAP-based method and system for generating association rules. An architecture is provided for processing transaction data to generate summary information, customer profiles, and association rules. The distributed system includes at least two layers of data warehouse/OLAP stations: local data-warehouse OLAP stations (LDOSs) and a global data-warehouse OLAP station (GDOS). The LDOSs perform local data mining and summarization, and the GDOS merges, mines, and summarizes the input data received from LDOSs. The summarized data is then utilized by the GDOS to generate association rules that can be provided to the LDOSs for business planning.

Abstract: A method of and an apparatus for designing a test environment and of evaluating performance of the test environment and an electronic device during testing of the electronic device. A virtual test environment is created emulating an actual test environment. A virtual device emulating the actual electronic device is implanted into the virtual test environment, and that virtual device is stimulated with an input test signal emulating the actual input signal applied to the actual electronic device in the actual test environment. The integrity of the input test signal and the resulting output signal is evaluated. An adjustment might be made to the virtual calibration of the virtual test environment and/or to the virtual device, or both, and the design of the actual device might be improved. The invention can be implemented on a properly programmed general purpose processing system or on a special purpose system.

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 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: Predictive modeling of consumer financial behavior, including determination of likely responses to particular marketing efforts, is provided by application of consumer transaction data to predictive models associated with merchant segments. The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Consumer vectors are developed within the vector space, to represent interests of particular consumers by virtue of relative vector positions of consumer and merchant vectors. Various techniques, including clustering, supervised segmentation, and nearest-neighbor analysis, are applied separately or in combination to generate improved predictions of consumer behavior.

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: Process for selecting a transmission channel and receiver of signals with antenna diversity A process for selecting a transmission channel from several transmission channels 1 and 2 of a receiver of OFDM radio signals with antenna diversity, with a view to favouring the transmission channel delivering a signal with the lowest binary error rate, consists in estimating the binary error rate for each transmission channel by feeding a neural network 9 with data RFC representative of the frequency response of the transmission channel.

Abstract: An improved neuron and corresponding search operation for use in matching strings of characters from a character set or strings of pixels from an image is at least partly based on ZISC technology. Each neuron contains only one character in the string of characters to be searched or, equivalently, one pixel in the image to be searched. The neurons are lined up in order (unlike standard ZISC). The inventive system matches two strings of base-pairs, one of which is stored in the neurons, and the other of which is entered into the system input one character at a time and thereafter broadcast to all of the neurons. The inputs, outputs and contents of each neuron in the system include one stored base pair, a left_errors register; a right_errors register; a parallel sort bus; and a neuron number or location register. The operation may include the following steps: at the start of the operation, all left_errors and right_errors registers are reset to “0”.

Abstract: A method and system for automating a process for valuing a property that produces an estimated value of a subject property, and a reliability assessment of the estimated value. The process is a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property's value. A network-based implementation of fuzzy inference is based on a system that implements a fuzzy system as a five-layer neural network so that the structure of the network can be interpreted in terms of high-level rules. The neural network is trained automatically from data. IF/THEN rules are used to map inputs to outputs by a fuzzy logic inference system. Different models for the same problem can be obtained by changing the inputs to the neuro-fuzzy network, or by varying its architecture.

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 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: 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: An apparatus, program product, and method utilize a dynamic, automated, extensible and flexible intelligent agent-based product support “framework” to facilitate the provision of product support services to customers of computer-related products. Agent platforms are utilized on both a customer computer and a product support computer, with different agents configured to execute on one or both of such computers. Automated analysis and/or creation of remedial actions, based upon cross-customer operational data, and often relying upon intelligent agents for many or all of such actions, may be used to decrease product support personnel burden and effort. Publishing control may be provided to limit distribution of certain intelligent agents, as may multi-agent remediation of undesirable operational conditions, where agents from multiple parties collectively address a problem experienced by a customer.

Abstract: An apparatus, program product, and method utilize distribution control information associated with intelligent agents to manage the distribution of such intelligent agents. Such distribution management may be incorporated into a dynamic, automated, extensible and flexible intelligent agent-based product support “framework” to facilitate the provision of product support services to customers of computer-related products. In such a framework, agent platforms may be utilized on both a customer computer and a product support computer, with different agents configured to execute on one or both of such computers.

Abstract: A neural network construct is trained according to sets of input signals (descriptors) generated by conducting a first experiment. A genetic algorithm is applied to the construct to provide an optimized construct and a CHTS experiment is conducted on sets of factor levels proscribed by the optimized construct.

Abstract: A retrieval system enables high-speed and high-precision pattern retrieval. A retrieval dictionary generation unit classifies learned patterns into plural clusters, and generates a retrieval dictionary using the clusters. A nearest cluster detector detects, a cluster nearest to an input retrieval patter from among clusters in a multidimensional space. The specific space is generated between a couple of spheres having radiuses smaller and larger, respectively, than a distance from a central cluster that locates near the center of the multidimensional space to the retrieval pattern. A learned pattern detector compares all learned patterns that belong to the nearest cluster with the retrieval pattern and detects a learned pattern at predetermined distance from the retrieval pattern. A retrieval range decision unit decides a retrieval range using the learned pattern detected by the learned pattern detector and retrieval pattern.

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 of operating a target system wherein small changes in input variables produce small changes in output variables in a manner permits system learning on a time dependent basis. The rate of change of the system output is directly dependent upon the product of the rate of change of the system input and a matrix consisting of input variables and antecedent and consequent parameters. A preferred performance criterion is obtained through the approximation of the said matrix to a weighted-augmented pseudo-inverse Jacobian. Off-line, the system undergoes a series of iterations using a wide range of input variables wherein the actual outputs are compared to the desired outputs and optimized values for the antecedent and consequent parameters are obtained and passed back to the said matrix for use in the subsequent iteration.

Abstract: Predictive modeling of consumer financial behavior is provided by application of consumer transaction data to predictive models associated with merchant segments. Merchant segments are derived from consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors representing specific merchants are clustered to form merchant segments in a vector space as a function of the degree to which merchants co-occur more or less frequently than expected. Each merchant segment is trained using consumer transaction data in selected past time periods to predict spending in subsequent time periods for a consumer based on previous spending by the consumer. Consumer profiles describe summary statistics of consumer spending in and across merchant segments. Analysis of consumers associated with a segment identifies selected consumers according to predicted spending in the segment or other criteria, and the targeting of promotional offers specific to the segment and its merchants.

Abstract: An autonomic system for updating a fuzzy neural network includes a process of calculating an estimated value based on fuzzy inference by using a neural network structure, wherein a parameter to be adjusted or identified by fuzzy inference and outputted from the neural network is made to correspond to coupling loads which are updated by learning, i.e., fuzzy rules and membership functions are adjusted by learning. This system is characterized in that the addition and deletion of fuzzy rules are conducted based on changes in output errors in an autonomic manner, thereby effectively obtaining appropriate numbers of fuzzy rules optimal for an object such as a vehicle engine having strong non-linearity. Fuzzy rules are formed by a combination of membership functions representing variables such as an engine speed and a throttle angle.

Abstract: Fuzzy logic controller for a queue manager that services a plurality of queues in an iterative fashion, each queue containing an indeterminate number of elements, the manager iteratively servicing each of the plurality of queues in accordance with a dequeuing quantity parameter, Q. The controller comprises a device for determining an amount of queues having remaining elements after each servicing iteration and outputting the amount; a device for comparing the difference between a system output representing an amount of queues having remaining elements in a current iteration with a previous output representing an amount of queues having remaining elements in a predecessor iteration, the output difference indicating a first dequeuing trend; and, a device for adjusting the dequeuing quantity parameter Q based on the dequeuing trend, the adjusting device capable of driving the system output toward a predetermined setpoint quantity after each servicing iteration.

Abstract: Bookmark sets are provided to a browser in a computer system by downloading a respective bookmark set as a unit. The process begins by creating a bookmark set at a first computer system computer system coupled to a network, the bookmark set containing a set of Uniform Resource Locators (URLs) and downloadable as a unit one or more computer systems. A request for downloading the bookmark set results in sending the bookmark set via the network to a second computer system. The bookmark set is received and used at the second computer system by a browser to access the set of URLs. The request for downloading the bookmark set can be initiated at the first computer system as a push operation or at the second computer system in a pull operation. The bookmark set can be set for automatic update; if changes are made at the first computer system, the updated bookmark set to the second computer system. The bookmark set can be incorporated in a downloaded web document.

Abstract: An electronic data mining process for mining from an electronic data base using an electronic digital computer a listing of commercially useful information of the type known in the art as an association rule containing at least one uninstantiated condition. For example, the commercially useful information may be information useful for sales promotion, such as promotion of telephone usage. The computer retrieves from the database a plurality of stored parameters from which measures of the uninstatiated condition can be determined. The computer uses a dynamic programming algorithm and iterates over intervals or sub-ranges of the parameters to obtain what is called an at least partially optimized association rule, as optimized intervals or sub-ranges of at least some of the retrieved parameters, for example, time intervals of high usage of certain types of telephone connections. These optimized intervals are provided as the listed commercially useful information.

Abstract: A new method to analyze and predict the binding energy for enzyme-transition state inhibitor interactions is presented. Computational neural networks are employed to discovery quantum mechanical features of transition states and putative inhibitors necessary for binding. The method is able to generate its own relationship between the quantum mechanical structure of the inhibitor and the strength of binding. Feed-forward neural networks with back propagation of error can be trained to recognize the quantum mechanical electrostatic potential at the entire van der Waals surface, rather than a collapsed representation, of a group of training inhibitors and to predict the strength of interactions between the enzyme and a group of novel inhibitors. The experimental results show that the neural networks can predict with quantitative accuracy the binding strength of new inhibitors.

Abstract: In data transfer connections with disturbances, e.g. in cellular networks, it is possible to establish and maintain an optimal connection using the solution according to the present invention. The optimal parameters for each data transfer connection are saved in the modem database. When the user next calls a number, the data of which has been saved, the parameters saved in the database are first used for the connection control. Interference occurring during the data transfer is handled with the help of the database with adaptive logic searching for the optimal data transfer rate and other connection parameters.

Abstract: An inference rule determining process according to the present invention sequentially determines, using a learning function of a neural network model, a membership function representing a degree which the conditions of the IF part of each inference rule is satisfied when input data is received to thereby obtain an optimal inference result without using experience rules. The inventive inference device uses an inference rule of the type "IF . . . THEN . . ." and includes a membership value determiner (1) which includes all of IF part and has a neural network; individual inference quantity determiners (21)-(2r) which correspond to the respective THEN parts of the inference rules and determine the corresponding inference quantities for the inference rules; and a final inference quantity determiner which determines these inference quantities synthetically to obtain the final results of the inference.