Adaptive evolution of holon networks by an autonomous decentralized method

Applied Mathematics and Computation

Volumn 91, Issue 1, 1998, Pages 43-61

  Honma, Noriyasu ^a   Abe, Kenichi ^a   Sato, Mitsuo ^b   Takeda, Hiroshi ^c  

^a TOHOKU UNIVERSITY   (Japan)

^b TOHOKU INSTITUTE OF TECHNOLOGY   (Japan)

^c TOHOKU GAKUIN UNIVERSITY   (Japan)

Author keywords

Autonomous decentralized systems and self organization; Chaos; Complex adaptive systems; Evolution; Recurrent networks; System identification

Indexed keywords

CHAOS THEORY; HEURISTIC METHODS; IDENTIFICATION (CONTROL SYSTEMS); LARGE SCALE SYSTEMS; NONLINEAR SYSTEMS;

AUTONOMOUS DECENTRALIZED METHOD; HOLON NETWORKS; RECURRENT NETWORKS;

ADAPTIVE SYSTEMS;

EID: 0032046224     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0096-3003(97)10008-X     Document Type: Article

References (16)

1. R. Ruthen, Adapting to complexity, Scientific American, January (1993) 110-117.
2. C.G. Langton, Computation at the edge of chaos: Phase transitions and emergent computation, Physica D 42 (1990) 12-37.
3. S.A. Kauffman, Antichaos and adaptation, Scientific American, August (1991) 64-70.
4. K.P. Unnikrishnan, K.P. Venugopal, Alopex: A Correlation-based learning algorithm for feedforward and recurrent neural networks, Neural Computation 6 (1994) 469-490.
5. J. Horgan, From complexity to perplexity, Scientific American, June (1995) 74-79.
6. P. Bak, K. Chen, Self-organized criticality, Scientific American, January (1991) 26-33.
7. T. Kitamori, Decentralized Autonomous systems and Self-Organization - Viewed from Actual Instances, Journal of SICE 32 (10) (1993) 797-800 (in Japanese).
8. A. Koestler, Janus, Hutchinson & Co Ltd, 1978.
9. N. Honma, M. Sato, K. Abe, H. Takeda, On autonomous decentralized evolution of holon network, Proceedings of the Ninth KACC International Session, 1994, pp. 498-503.
10. S. Wolfram, Universality and complexity in cellular automata, Physica D 10 (1984) 1-35.
11. J.J. Hopfield, D.W. Tank, Neural computation of decisions in optimization problems. Biological Cybernetics 52 (1985) 141-152.
12. F.J. Pineda, Generalization of back-propagation to recurrent neural networks, Phys. Rev. Lett. 59 (19) (1987) 2229-2232.
13. B.A. Pearlmutter, Learning state space trajectories in recurrent neural networks, Neural Computation 1 (2) (1989) 263-269.
14. R.J. Williams, D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation 1 (2) (1989) 270-280.
15. D.E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley, Manning, MA, 1989.
16. N. Honma, M. Sato, K. Abe, H. Takeda, On evolution of holon network by using autonomous decentralized adaptation method, Trans. SICE 32 (7) (1995) 908-915 (in Japanese).