Indiana University

Advanced Research & Technology Institute

Gm Global Technolgoy Operations, Inc.

USA As Represented by Secretary of the Navy

Russell C Eberhart

Xiaohui Hu

Patrick Shaffer

Jonathan W Mills

Ronald David Brost

Michael Scott Sullivan

Bryce Himebaugh

Yaobin Chen

Robert M Worth

Yuhui Shi

Embodiments of a system and a method for assigning a plurality of resources to a plurality of targets in near real-time are described herein. An evolutionary algorithm such as a particle swarm algorithm iteratively evaluates a predetermined fitness function characteristic of a system including the plurality of resources to find solutions containing optimal resource assignments. Resource parameters are changed based on the assignments to operate on the targets.

Real-time optimization of allocation of resources

A method is used to configure an extended analog computer for use as an application controller. The method includes selecting input pins from among a plurality of pins in a continuous sheet processor, selecting an arrangement of intermediate and output pins from among the remaining pins in the plurality of pins in the continuous sheet processor, applying a pattern data set to the input pins, using an evolutionary algorithm, coupling current sources and sinks to the intermediate and output pins, measuring an error between an output and its expected value, and continuing to select intermediate and output pin arrangements, apply pattern data sets, and measure errors until a configuration threshold is met.

Method and apparatus for evolving overlays to operate an extended analog computer as a classifier or a controller

A method of diagnosing patients suspected of having a neurological disorder is disclosed and includes monitoring movement of a patient in order to obtain movement data that is representative of the movement of the patient. Another step of the method includes processing the movement data in order to obtain an input pattern that is representative of the movement data. The method also includes the step of processing the input pattern with a computational intelligence system that has been trained to classify movement based upon a predetermined group of neurological disorder classifications. Furthermore, the method includes generating with the computational intelligence system an output that is indicative of an appropriate neurological disorder classification for the patient. An analysis system for diagnosing patients suspected of having a neurological disorder is also disclosed.

Method and apparatus utilizing computational intelligence to diagnose neurological disorders

A method of evolving a neural network that includes a plurality of processing elements interconnected by a plurality of weighted connections includes the step of obtaining a definition for the neural network by evolving a plurality of weights for the plurality of weighted connections, and evolving a plurality of activation function parameters associated with the plurality of processing elements. Another step of the method includes determining whether the definition for the neural network may be simplified based upon at least one activation function parameter of the plurality of activation function parameters. Yet another step of the method includes updating the definition for the neural network in response to determining that the definition for the neural network may be simplified. The method utilizes particle swarm optimization techniques to evolve the plurality of weights and the plurality of activation parameters. Moreover, the method simplifies activation functions of processing elements in response to corresponding activation parameters meeting certain criteria, and removes processing elements from the definition of the neural network in response to corresponding activation parameters satisfying certain criteria. Various apparatus are also disclosed for implementing network evolution and simplification.

Method and apparatus for evolving a neural network

A neural network characterized by a minimal architecture suitable for implementation in conventional microprocessor battery pack monitoring hardware includes linear and non-linear processing elements and battery parameter measurements representative of real time and temporal quantities whereby state of charge estimations actually converge with 100% and 0% states-of-charge.

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Indianapolis, IN, United States of America

Russell C Eberhart