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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Analog hardware for delta-backpropagation neural networks

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift.

Eberhardt, Silvio P.↗
NASA NTRS1992

Fault tolerance of artificial neural networks with applications in critical systems

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Protzel, Peter W.↗
NASA NTRS1991

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Ross, Muriel D.↗
NASA NTRS1992

Fault isolation detection expert (FIDEX). Part 1: Expert system diagnostics for a 30/20 Gigahertz satellite transponder

LeRC has recently completed the design of a Ka-band satellite transponder system, as part of the Advanced Communication Technology Satellite (ACTS) System. To enhance the reliability of this satellite, NASA funded the University of Akron to explore the application of an expert system to provide the transponder with an autonomous diagnosis capability. The results of this research was the development of a prototype diagnosis expert system called FIDEX (fault-isolation and diagnosis expert). FIDEX is a frame-based expert system that was developed in the NEXPERT Object development environment by Neuron Data, Inc. It is a MicroSoft Windows version 3.0 application, and was designed to operate on an Intel i80386 based personal computer system.

Durkin, John↗
NASA NTRS1991

Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

Lee, Chuen-Chien↗
NASA NTRS1992

The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

Deyong, Mark R.↗
NASA NTRS1992

Effects of spaceflight on hypothalamic peptide systems controlling pituitary growth hormone dynamics

Possible effects of reduced gravity on central hypophysiotropic systems controlling growth hormone (GH) secretion were investigated in rats flown on Cosmos 1887 and 2044 biosatellites. Immunohistochemical (IHC)staining for the growth hormone-releasing factor (GRF), somatostatin (SS), and other hypothalamic hormones was performed on hypothalami obtained from rats. IHC analysis was complemented by quantitative in situ assessments of mRNAs encoding the precursors for these hormones. Data obtained suggest that exposure to microgravity causes a preferential reduction in GRF peptide and mRNA levels in hypophysiotropic neurons, which may contribute to impared GH secretion in animals subjected to spaceflight. Effects of weightlessness are not mimicked by hindlimb suspension in this system.

Sawchenko, P. E.↗
NASA NTRS1993

Model Of Neural Network With Creative Dynamics

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

Zak, Michail↗
NASA NTRS1993

Neurodynamics With Spatial Self-Organization

Report presents theoretical study of dynamics of neural network organizing own response in both phase space and in position space. Postulates several mathematical models of dynamics including spatial derivatives representing local interconnections among neurons. Shows how neural responses propagate via these interconnections and how spatial pattern of neural responses formed in homogeneous biological neural network.

Zak, Michail A.↗
NASA NTRS1992

Spacecraft attitude control using a smart control system

Traditionally, spacecraft attitude control has been implemented using control loops written in native code for a space hardened processor. The Naval Research Lab has taken this approach during the development of the Attitude Control Electronics (ACE) package. After the system was developed and delivered, NRL decided to explore alternate technologies to accomplish this same task more efficiently. The approach taken by NRL was to implement the ACE control loops using systems technologies. The purpose of this effort was to: (1) research capabilities required of an expert system in processing a classic closed-loop control algorithm; (2) research the development environment required to design and test an embedded expert systems environment; (3) research the complexity of design and development of expert systems versus a conventional approach; and (4) test the resulting systems against the flight acceptance test software for both response and accuracy. Two expert systems were selected to implement the control loops. Criteria used for the selection of the expert systems included that they had to run in both embedded systems and ground based environments. Using two different expert systems allowed a comparison of the real-time capabilities, inferencing capabilities, and the ground-based development environment. The two expert systems chosen for the evaluation were Spacecraft Command Language (SCL), and NEXTPERT Object. SCL is a smart control system produced for the NRL by Interface and Control Systems (ICS). SCL was developed to be used for real-time command, control, and monitoring of a new generation of spacecraft. NEXPERT Object is a commercially available product developed by Neuron Data. Results of the effort were evaluated using the ACE test bed. The ACE test bed had been developed and used to test the original flight hardware and software using simulators and flight-like interfaces. The test bed was used for testing the expert systems in a 'near-flight' environment. The technical approach, the system architecture, the development environments, knowledge base development, and results of this effort are detailed.

Buckley, Brian↗
NASA NTRS1992

Membership generation using multilayer neural network

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

Kim, Jaeseok↗
NASA NTRS1993

Neural-network dedicated processor for solving competitive assignment problems

A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques.

Eberhardt, Silvio P.↗
NASA NTRS1993

Efficiently modeling neural networks on massively parallel computers

Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.

Farber, Robert M.↗
NASA NTRS1991

Neural networks: What non-linearity to choose

Neural networks are now one of the most successful learning formalisms. Neurons transform inputs (x(sub 1),...,x(sub n)) into an output f(w(sub 1)x(sub 1) + ... + w(sub n)x(sub n)), where f is a non-linear function and w, are adjustable weights. What f to choose? Usually the logistic function is chosen, but sometimes the use of different functions improves the practical efficiency of the network. The problem of choosing f as a mathematical optimization problem is formulated and solved under different optimality criteria. As a result, a list of functions f that are optimal under these criteria are determined. This list includes both the functions that were empirically proved to be the best for some problems, and some new functions that may be worth trying.

Kreinovich, Vladik YA.↗
NASA NTRS1992

Stochastic architecture for Hopfield neural nets

An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.

Pavel, Sandy↗
NASA NTRS1992

Model for the computation of self-motion in biological systems

A technique is presented by which direction- and speed-tuned cells, such as those commonly found in the middle temporal region of the primate brain, can be utilized to analyze the patterns of retinal image motion that are generated during observer movement through the environment. The developed model determines heading by finding the peak response in a population of detectors or neurons each tuned to a particular heading direction. It is suggested that a complex interaction of multiple cell networks is required for the solution of the self-motion problem in the primate brain.

Perrone, John A.↗
NASA NTRS1992

Antagonistic otolith-visual units in cat vestibular nuclei

The nature of neural coding of visual (Vis) and vestibular (Vst) information on translational motion in the region of the vestibular nuclei was investigated using extracellular single-unit recordings in alert adult cats. Responses were recorded and averaged over 60 cycles of stimulation in the vertical and horizontal planes, which included the Vst (movement of the animal in the dark), Vis (movement within lighted visual surround), and combined Vis and Vst (movement of the animal within the lighted stationary visual surround). Data are reported on responses to stimulations along the axis showing maximal sensitivity. A small number of units were identified that showed an antagonistic relationship between their Vis and Vst responses (since they were maximally excited by Vis and by Vst stimulations in the same direction). Results suggest that antagonistic units may belong to an infrequently encountered, but functionally distinct, class of neurons.

Daunton, Nancy G.↗
NASA NTRS1988

Effects of systemic L-tyrosine on dopamine release from rat corpus striatum and nucleus accumbens

Intracerebral dialysis was used to monitor extracellular fluid from rat striatum and nucleus accumbens following the intraperitoneal administration of tyrosine. Dopamine concentrations in dialysates from both the striatum and the nucleus accumbens increased significantly in response to the tyrosine. The magnitude of the tyrosine effect was greater in the nucleus accumbens than in the striatum. Hence, mesolimbic dopaminergic neurons may be especially responsive to precursor availability.

During, Matthew J.↗