1995-04-17
1996-11-12
Davis, George B.
395 21, 395 23, G06F 1518
Patent
active
055748279
ABSTRACT:
A method is implemented in hardware or software type neural network, the neural network is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. Each neuron applying a gating function to each of neural network inputs. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.
REFERENCES:
patent: 3638196 (1972-01-01), Nishiyama et al.
patent: 3922536 (1975-11-01), Hampel et al.
patent: 3967100 (1976-06-01), Shimomura
patent: 4156922 (1979-05-01), Majerski et al.
patent: 4660166 (1987-04-01), Hopfield
patent: 4874963 (1989-10-01), Alspector
patent: 4912649 (1990-03-01), Wood
patent: 4949292 (1990-08-01), Hoshino et al.
patent: 5042001 (1991-08-01), Brightman et al.
patent: 5046020 (1991-09-01), Filkin
patent: 5058180 (1991-10-01), Khan
patent: 5073867 (1991-12-01), Murphy et al.
patent: 5103405 (1992-04-01), Murphy et al.
patent: 5159660 (1992-10-01), Lu et al.
patent: 5166539 (1992-11-01), Uchimura et al.
patent: 5210825 (1993-05-01), Kavaler
patent: 5239594 (1993-08-01), Yoda
patent: 5251286 (1993-10-01), Wiener et al.
patent: 5263121 (1993-11-01), Melsa et al.
patent: 5265192 (1993-11-01), McCormack
patent: 5267151 (1993-11-01), Ham et al.
patent: 5278945 (1994-01-01), Basehore et al.
patent: 5293457 (1994-03-01), Arima et al.
patent: 5323470 (1994-06-01), Kara et al.
patent: 5333239 (1994-07-01), Watanabe et al.
patent: 5333240 (1994-07-01), Matsumoto et al.
patent: 5359700 (1994-10-01), Seligson
patent: 5412670 (1995-05-01), Stork et al.
Output Weight Optimization for the Multi-layer Perceptron, M. T. Manry et al, Conf. Record of the 26th Asilomav Conf. on Signals, Systems, and Computers, 26-28 Oct. 1992, pp. 502-506.
Polynomial and Standard Higher Order Neural Network, Chir-Ho Chang, Jin-Ling Lin, and J. Y. Cheung, Electrical Engineering and Computer Science, University of Oklahoma, 1993 IEEE International Conference on Neural Networks, Mar. 28-Apr. 1, 1993, pp. 989-994.
A Neural Feed-Forward Network with a Polynomial Nonlinearity, Nils Hoffmann, Electronics Institute, Building 349, Technical University of Denmark, DK-2800 Lyngby, Denmark, Neural Networks for Signal Processing, Proceedings of the IEEE-SP Workshop, Aug. 31-Sep. 2, 199?, pp. 49-58.
A Polynomial Time Algorithm for Generating Neural Networkes for Classification Problems, Asim Roy and Somnath Mukhopadhyay, Dept. of Decision and Information Systems, Arizona State University, IJCNN, 1992, 0-7803-0559-0/92 IEEE, pp. I-147-I-152.
Modeling systems with Polynomial Networks, Tools for Predicting Behavior, Peter D. Varhol, Programmer's Workbench, Dr. Dobb's Journal, Sep. 1993, Begins on p. 76.
AIM Outperforms Neural Networks and Regression, Mark Shewhart, USAF (IEEE Spectrum), AbTech Corporation, Charlotteville, VA 22903.
Polynomial Functions Can Be Realized by Finite Size Multilayer Feedforward Neural Networks, Naohiro TODA, Ken-ichi FUNAHASHI and Shiro USUI, Department of Information and Computer Sciences, Toyohashi University of Technology, Tempaku, Toyohashi 441, Japan, 1991 IEEE International Joint Conference on Neural Networks, vol. 1 of 3, The Westin Stamford and Westin Plaza, 18-21, Nov. 1991, Singapore.
An Artificial Neural Networks for Approximating Polynomial Functions, Behnam Malakooti and YingQing Zhou, Department of Systems Engineering, Center for Automation and Intelligent Systmes Research, Case Western Reserve University, Cleveland, Ohio, International Joint Conference on Neural Networks, Jun. 1992, pp. III-966-III-971.
Recursive Reduction in Finite Ring Computations, D. Zhang, G. A. Jullien, W. C. Miller, VLSI Research Group, Department of Electrical Engineering, University of Windsor, Windsor, Ontario Canada N9B 3P4, Twenty-Third Asilomar conf. on Signals, systems and Computers, Conference Record vol. 2 of 2, pp. 854-857.
Robot Kinematics Learning Computations Using Polynomial Neural Networks, C. L. Philip Chen and A. D. McAulay, Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, Proceedings of the 1991 IEEE, International Cof. on Robotics and Automation, Sacramento, CA, Apr. 1991, pp. 2638-2643.
Backpropagation Based on the Logarithmic Error Function and Elimination of Local Minima, Kiyotoshi Matsuoka and Jianqiang Yi, Department of Control Engineering, Kyushu Institute of Technology, Sensul 1-1. Tobata. Kitakyushu, 804 Japan, CH3065-0/91/0000-1117 IEEE, pp. 1117-1122.
Highly-automated, Non-parametric statistical learning for autonomous target recognition, Keith C. Drake, AbTech Corporation, 700 Harris Street, Charlottesville, Virginia 22903, pp. 1-10, Proceedings of the SPI 20th Applied Imagery Pattern Recognition Workshop, Oct. 1991, McLean, Virginia.
Generalization and Learning in Volterra and Radial Basis Function Networks: A Theoretical Analysis, Sean B. Holden and Peter J. W. Rayner, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, U.K., 0-7803-0532-9/92, 1992 IEEE, pp. II-273-II-276.
On the Design Principles of the Functional Link Nets, Jun Wang and Edilberto P. Teixeira, Department of Systems Engineering, Case Western Reserve University, Cleveland, Ohio 44106, pp. 613-616, Ieee International Conf. on Systems Engineering, Aug. 9-11, 1990, Vista International Hotel, Pittsburgh, Pennsylvania, IEEE Catalog No.:90CH2872-0.
An Introduction to Computing with Neural Nets, Richard P. Lippmann, IEEE ASSP Magazine, Apr. 1987, pp. 4-22.
Approximation Property of Multi-Layer Neural Network (MLNN) and Its Application in Nonlinear Simulation, Zhang Wei et al., IJC VN, Jul. 1991, pp. 8-14.
Training Product Unit Neural Networks with Genetic Algorithms, David J. Janson et al., IEEE Expert, Oct. 1993, pp. 26-32.
Neural Networks are Ready for Prime Time, Jack Shandle, Electronic Design, Advanced Technology Series, Feb. 18, 1993, pp. 51-58.
Davis George B.
Lindsey Michael K.
Motorola Inc.
Nielsen Walter W.
Stuckman Bruce E.
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