Abstract: Efficiently simulating an Amari dynamics of a neural field (a), the Amari dynamics being specified by the equation (1) where a(x,t) is the state of the neural field (a), represented in a spatial domain (SR) using coordinates x,t, i(x,i) is a function stating the input to the neural field at time t, f[.] is a bounded monotonic transfer function having values between 0 and 1, F(x) is an interaction kernel, s specifies the time scale on which the neural field (a) changes and h is a constant specifying the global excitation or inhibition of the neural field (a). A method for simulating an Amari dynamics of a neural field (a), comprising the step of simulating an application of the transfer function (f) to the neural field (a). According to the invention, the step of simulating an application of the transfer function (f) comprises smoothing the neural field (a) by applying a smoothing operator (S).

Abstract: A system to move a component in accordance with a setpoint profile including a plurality of target states of the component, each of the plurality of target states to be substantially attained at one of a corresponding sequence of target times, is presented. The system includes a displacement device to move the component according to the setpoint profile; a storage device containing a library of feedforward data; a signal generating part configured to identify a plurality of time segments of the setpoint profile that correspond to entries in the library of feedforward data, and access the entries in order to construct a feedforward signal; and a feedforward control system to control the operation of the displacement device by reference to the feedforward signal constructed by the signal generating part.

Abstract: We develop a system consisting of a neural architecture resulting in classifying regions corresponding to users' keystroke patterns. We extend the adaptation properties to classification phase resulting in learning of changes over time. Classification results on login attempts of 43 users (216 valid, 657 impersonation samples) show considerable improvements over existing methods.

Abstract: Computer-implemented methods, computer-readable storage media, and systems for control of a plant provide a plurality of repeating interconnected structures that can reduce software coding complexity, a limbic module that can provide an operational change in a type of control resulting in improved control flexibility in unknown environments, and different hierarchical levels of behavioral control that can offload some processing to rote control.

Abstract: A Digital Multi-Source Multi-Destination Video Multiplexer and Crossbar Device and method which may be applied to a video imaging system, in which multiple input image sources including super-computer-generated digital image scenes, personal computer generated digital image scenes, or any machine with OC-48 fiber optically generated digital scenes are multiplexed, bit combined, and output as a selection of multiple digital video output formats.

Abstract: An adaptive system modeling method is provided. A system model is generated by using data corresponding to an input features set selected by using a baseline significance signature of the system. A superset of the input features and other features also is selected by using the baseline significance signature. Data collected from the system corresponding to the superset is maintained online. A new significance signature of the system is periodically or intermittently determined by performing a discriminant analysis using the online superset data, and is used to detect an evolutionary change in the system.

Abstract: A cortical simulator optimizing the simulation scale and time through computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion, memory efficient representation of simulation state, and communication efficient message exchanges.

Abstract: A mobile brain-based device BBD includes a mobile base equipped with sensors and effectors (Neurally Organized Mobile Adaptive Device or NOMAD), which is guided by a simulated nervous system that is an analogue of cortical and sub-cortical areas of the brain required for visual processing, decision-making, reward, and motor responses. These simulated cortical and sub-cortical areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. The brain-based device BBD learns to discriminate among multiple objects with shared visual features, and associated “target” objects with innately preferred auditory cues. Globally distributed neuronal circuits that correspond to distinct objects in the visual field of NOMAD 10 are activated. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, result in successful discrimination of objects.

Abstract: A cortical simulator optimizing the simulation scale and time through computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion, memory efficient representation of simulation state, and communication efficient message exchanges.

Abstract: A method is provided for a virtual sensor system. The method may include establishing a first process model indicative of interrelationships between a plurality of input parameters and a plurality of output parameters and establishing a second process model indicative of interrelationships between at least the plurality of input parameters and modeling errors of the first process model. The method may also include operating the first process model to generate values of the plurality of output parameters and simultaneously operating the second model to generate estimated deviations between the values of the plurality of output parameters and desired values of the plurality of output parameters. Further, the method may include compensating the values of the plurality of output parameters with the estimated deviations to generate the desired values of the plurality of output parameters.

Abstract: A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval/disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli.

Abstract: A design of a tire can be facilitated. An optimization apparatus 30 inputs known design parameters of the shape, structure, and pattern of a tire, and performances thereof by an experimental data input unit 40 and learns, as a conversion system of a neural network, a correlation between design parameters of the shape, structure, and pattern of the tire, and performances thereof. Ranges which constrain performances of the tire and design parameters of the shape, structure, and pattern of the tire, which are to be optimized, are inputted in an optimization item input unit 42, and the performances of the tire are predicted in an optimization calculation unit 34 from the design parameters of the shape, structure, and pattern of the tire by using the optimization item and models of the calculation unit 32, and an objective function is optimized until the objective function which is the performances of the tire is converged.

Abstract: A method for the detection and combating of forest and surface fires includes the steps of observing and detecting fires using an infrared camera on board an observation aircraft; georeferencing image obtained by the infrared camera pixel-wise using location data of the observation aircraft as obtained by a satellite navigation system; testing the georeferenced infrared image for hot points caused by a fire, and transmitting coordinates of the hot points via a data link to a central data processing system on the ground; automatically generating deployment plans for available firefighting vehicles in the central data processing system taking into consideration data relating to the terrain and data on available firefighting equipment; transferring the deployment plans generated in the central data processing system to on-board management systems of deployed vehicles; representing deployment data and coordinates corresponding to the deployment plans with output apparatus by the on-board management systems of the

Abstract: Disclosed is a nervous system-based emotion synthesizing apparatus which enables an emotion of a humanoid to be synthesized in a manner similar to that of a human using a nervous system-adjusting mechanism of a human body according to emotions.

Abstract: A controller directs the operation of an air pollution control (APC) system having one or more controllable operating parameters and a defined operating limit representing a regulatory limit on an allowed amount of pollutant to be emitted by the APC system. An interface receives data representing a value of a regulatory credit available for emitting less of the pollutant than the regulatory limit on the allowed amount of pollutant. A control processor (i) determines a target set point for each of at least one of the one or more controllable operating parameters, which will maximize the regulatory credits earned, based on the received data and (ii) to directs control of each of the at least one controllable operating parameter based on the determined target set point for that parameter.

Abstract: Provided are a method, system and program for using multiple data structures to manage data in cache. A plurality of data structures each have entries identifying data from a first computer readable medium added to a second computer readable medium. A request is received for data in the first computer readable medium. A determination is made as to whether there is an entry for the requested data in one of the data structures. The requested data is retrieved from the first computer readable medium to store in the second computer readable medium in response to determining that there is no entry for the requested data in one of the data structures. One of the data structures is selected in response to determining that there is no entry for the requested data in one of the data structures and an entry for the retrieved data is added to the selected data structure.

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: Convolutional networks can be defined by a set of layers being respectively made up by a two-dimensional lattice of neurons. Each layer—with the exception of the last layer—represents a source layer for respectively following target layer. A plurality of neurons of a source layer called a source sub-area respectively share the identical connectivity weight matrix type. Each connectivity weight matrix type is represented by a scalar product of an encoding filter and a decoding filter. For each source layer a source reconstruction image is calculated on the basis of the corresponding encoding filters and the activities of the corresponding source sub-area. For each connectivity weight matrix type, each target sub-area and each target layer the input of the target layer is calculated as a convolution of the source reconstruction image and the decoding filter.

Abstract: Method intended for real-time modelling, by neural networks, of hydrodynamic characteristics of multiphase flows in transient phase in pipes. In order to specifically take account of the possible flow regimes of fluids in pipes, various neural or “expert” models are formed for several flow regimes (or subregimes) in the whole of the variation range of the hydrodynamic characteristics of the flows (preferably for each one of them), as well as a neural model estimating the probability of belonging of the flows to each flow regime or subregime, knowing some of the characteristics thereof. The probabilities obtained are used for weighting the estimations delivered by each neural model, the result of the weighted sum being then the estimation eventually retained. Applications to various industries and notably for modelling of hydrocarbon flows in pipelines.

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

Abstract: A computer-implemented method and system for determining colimits of hereditary diagrams. A user specifies a diagram of diagram and specifies performance of a colimit operation. Once the colimit is performed, the name of the colimit is added to the hereditary diagram. The described embodiment supports diagrams of diagrams, also called hierarchical diagrams.

Abstract: The invention is a method and system for real-time estimation of the flow mode, at all points of a pipe whose structure is defined by a certain number of structure parameters, of a multiphase fluid stream defined by several physical quantities providing simplified implementation of hydrodynamic modules that can be integrated in modelling tools. A non-linear neural network is formed with an input layer having as many inputs as there are structure parameters and physical quantities, an output layer with as many outputs as there are quantities necessary for estimation of the flow mode and at least one intermediate layer. A learning base is created with predetermined tables connecting various values obtained for the output data to the corresponding values of the input data, with iterative determination of the weighting factors of the activation function allowing to properly connect the values in the input and output data tables.

Abstract: An apparatus and method for selecting the length of a variable length code bitstream by using a neural network are provided. The apparatus for selecting the length of a variable length code bitstream includes a bitstream estimation length receiving unit which inputs a predetermined quantization DCT coefficient block to a neural network whose training is finished, and receives the estimation length of a bitstream corresponding to the quantization DCT coefficient block from the neural network; and a bitstream estimation length selection unit which receives user selection about an estimation length received by the bitstream estimation length receiving unit. According to the method and apparatus the length of a variable length code bit stream can be estimated such that a user can select a desired length of a bitstream in advance without performing variable length coding.

Abstract: A circuit implementing a non-integer order dynamic system includes a neural network that receives at least one input signal and generates therefrom at least one output signal. The input and output signals are related to each by a non-integer order integro-differential relationship through the coefficients of the neural network. A plurality of such circuits, implementing respective non-integer order controllers can be interconnected in an arrangement wherein any of the integral or differential blocks included in one of these circuits generates a signal which is fed to any of the integral or differential blocks of another circuit in the system.

Abstract: A method for detecting fires and reacting thereto. A stationary, earth orbit satellite, drone aircrafts or piloted aircraft contains infrared detectors and optical means for detecting fires. A computer in the satellite, drone or piloted aircraft and/or on the ground analyzes image signals of the areas being monitored and, upon detecting infrared radiation of varying intensity and shape, indicative that a fire has started, generates signals which are indicative of the coordinate locations of the fire, the extent of the fire, the shape of the area(s) burning, the direction of movement of the fires, the speed(s) of the flame fronts, smoke condition, intensity of the fire, fire ball location(s), etc. In one form, a camera generates image signals which are digitized and recorded. Expert system logic is used to prioritize dangerous areas to assist in directing fire fighting.

Abstract: A neural network for predicting values in non-linear functional mappings having a single hidden layer function generator (12) and an output layer (40). The single hidden layer function generator (12) is operable to receive one or more mapping inputs (x1) and generate a plurality of terms (14) from each mapping input. The plurality of terms generated by the single hidden layer function generator (12) includes at least one trigonometric term selected from the group comprising sin(x1), sin(2x1), sin(3x1), cos(x1), cos(2xl), cos(3xl), cosec(xl), cotan(xl), and being free of Gaussian and Sigmoidal terms.

Abstract: To train the sequential kinematic behavior of an automated telekinegenesis robot system, a virtual reality simulator is driven with kinematic parameter data derived from a teleoperational device, which models the sequential behavior to be exhibited by the target robot. Sensor outputs of the teleoperational device are processed by a geometry conversion algorithm, to generate data representative of the spatial kinematics of the robot's desired travel path. A kinematic machine simulator program within a robotic control simulation workstation simulates a virtual machine based upon the actual parameters of the robot. The virtual reality simulation program is interactive, allowing the workstation operator to selectively interrupt the operation of the machine, modify its control parameters, and then rerun the program, until the desired behavior of the target machine is achieved.

Abstract: An elastic robot body in which a number of actuators are integrated. If the actuators are triggered individually or in groups, they exercise a force on the material of the body, so that the latter changes its shape. Integrated sensors or switches measure the momentary position of the body. An imitation hand, comprising such a body, can move its sections in a human fashion.

Abstract: A problem solving unit for obtaining a solution in a symbol process in response to a given problem is provided with a learning control unit for making a neural network learn the solution output from the problem solving unit. The output in response to the given problem from the learned neural network is transmitted to the problem solving unit as a (first) hint on obtaining the solution to the problem. Furthermore, a second neural network for outputting a second hint is provided, selects either the first hint or the second hint, and provides the selection result to the problem solving unit.

Abstract: In a method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior wherein only a few measured values of the influencing variable are available and the remaining values of the time series are modelled, a combination of a non-linear computerized recurrent neural predictive network and a linear error model are employed to produce a prediction with the application of maximum likelihood adaption rules. The computerized recurrent neural network can be trained with the assistance of the real-time recurrent learning rule, and the linear error model is trained with the assistance of the error model adaption rule that is implemented on the basis of forward-backward Kalman equations. This model is utilized in order to predict values of the glucose-insulin metabolism of a diabetes patient.

Abstract: A device for the simulation of spinning machines with a view to their optimum economic use and operation. Starting from material properties of a preproduct (1) determined by measurement, as well as from configuration and setting parameters (3) of such a spinning machine, material properties (2) of the intermediate or output products which are being produced, which properties can be determined by measurement, are predicted using a process model describing the behavior of the spinning machine, by a simulation device. The process model on which a method being based is presented by a neural network. The coefficients determining the actual behavior of this neural network area calculated from a set of sample data in a training phase. This sample data and/or from otherwise conclusively predicted properties of the behavior of such a spinning machine.

Abstract: A method and system for controlling a propulsion system of a vehicle. The propulsion system includes at least one propulsion effector. The system also includes sensors for sensing vehicle position and motions, a control device for generating control signals, and a generator for generating desired vehicle forces/moments from the sensed vehicle position and motions and the generated control signals based on predefined vehicle compensation and control laws. Also included is a neural network controller for generating propulsion commands for the at least one propulsion effector based on the generated desire forces/moments, wherein said neural network controller was trained based on pregenerated vehicle control distribution data.

Abstract: Each of a plurality of units 2 has the learning section 9 which updates the parameters PRM and performs learning. The learning means 9 modifies the parameters PRM based on the error signal OS' propagated from one unit to another. To build a system consisting of such units 2, the user uses the GUI or some other method to select the units 2 as well as the signal paths for connecting the units via the user interface, displays the system configuration status, and adjusts the functions of units 2. Therefore, an easy-to-build data processing system and a system-build system and method used to build such a system can be provided.

Abstract: A neural processor includes neural calculation apparatus (30, NQ, RQ) which normalize an input data X with respect to another input data Y. It performs a division of X by Y in order to determine a quotient Q. The calculation apparatus are programmed to calculate (30) by iteration, a series of contributions .DELTA.Q.sub.i which are used (NQ, RQ) to update a partial quotient QP which becomes the quotient Q at the end of calculation. The calculation can be performed on an arbitrary arithmetic base which determines the number of neurons utilized and also the accuracy of calculation. It is also possible to utilize a partial remainder RP. Several programming modes are presented.