Adaptive Random Search Approach to Identification of Neural Network Model

Author:

HU Jinglu1,Hirakawa Kotaro1

Affiliation:

1. Graduate School of Information Science and Electrical Engineering, Kyushu University

Publisher

The Institute of Systems, Control and Information Engineers

Subject

General Medicine

Reference11 articles.

1. [1] R.L. Watrous, “Learning algorithm for connectionist networks: Applied gradient methods of nonlinear optimization”, in Proc. of IEEE International Conference on Neural Networks, 1987, vol. 2, pp. 619-628.

2. [2] J. Matyas, “Random optimization”, Automation and Remote Control, vol. 28, pp. 244-251, 1965.

3. [3] E.B. Bartlett, “A stochastic training alogrithm for artificial neural networks”, Neurocomputing, vol.6, no. 1, pp. 31-43, 1994.

4. [4] S. Kirkpatrick, C. D. Gelatt, and M.P. Uecchi, “Optimization by simulated annealing”, Sicence, vol. 220, pp. 671-680, 1983.

5. [5] D.E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley Publishing Company, Inc., 1989.

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