Please use this identifier to cite or link to this item:
http://acervodigital.unesp.br/handle/11449/33563
- Title:
- Nonlinear optimization using a modified Hopfield model
- Universidade Estadual Paulista (UNESP)
- Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving constrained nonlinear optimization problems. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach.
- 1-Jan-1998
- IEEE World Congress on Computational Intelligence. New York: IEEE, p. 1629-1633, 1998.
- 1629-1633
- Institute of Electrical and Electronics Engineers (IEEE)
- http://dx.doi.org/10.1109/IJCNN.1998.686022
- Acesso restrito
- outro
- http://repositorio.unesp.br/handle/11449/33563
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