K. Kobayashi and T. Torioka, "A Wavelet Neural Network for Function Approximation and Network Optimization," Intelligent Engineering Systems Through Artificial Neural Networks, Volume 4, C. H. Dagli, B. R. Fernandez, J. Ghosh, and R. T. Soundar Kumara, Eds., Proceedings of the Artificial Neural Networks in Engineering (ANNIE `94) Conference, pp. 505-10, 1994. Home/Search Document Details and Download
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Designing Wavelet Networks Using Genetic Algorithms - Kobayashi, Torioka Self-citation (Torioka) (Correct)
....increases. Therefore, it is crucial to determine the optimal network size for realizing desired mappings. Recently, the methods for designing the networks using wavelet analysis have been proposed. Wavelet networks have the following merits [Pati and Krishnaprasad 1993; Zhang and Benveniste 1992; Kobayashi and Torioka 1994; Kobayashi, Torioka and Yoshida 1994] 1) It is guaranteed that wavelet networks can suciently approximate any functions in the Hilbert space L 2 (R) 2) The internal expressions are readily understandable because of their multi resolutional structures. 3) It is not dicult to add or delete ....
....transform[Pati and Krishnaprasad 1993] However, their method does not guarantee an optimal arrangement of windows because it uses only information of expansion coecients in network synthesis. In the present paper, we regard the arrangement of windows as a combinatorial optimization problem [Ueda, Kobayashi and Torioka 1997] Then we propose a new method to optimally arrange windows using genetic algorithms. Through computer experiments, it is veri ed that our method provide an optimal arrangement. 2 PRELIMINARIES In this section, the basics of wavelets is brie y introduced[Chui 1992] Throughout the present ....
[Article contains additional citation context not shown here]
Kobayashi and Torioka 1994. A Wavelet Neural Network for Function Approximation and Network Optimization, Vol.4, pp.505-510, Proceedings of ANNIE'94, AMSE Press.
Designing Wavelet Networks Using Genetic Algorithms - Kobayashi, Torioka Self-citation (Torioka) (Correct)
....increases. Therefore, it is crucial to determine the optimal network size for realizing desired mappings. Recently, the methods for designing the networks using wavelet analysis have been proposed. Wavelet networks have the following merits [Pati and Krishnaprasad 1993; Zhang and Benveniste 1992; Kobayashi and Torioka 1994; Kobayashi, Torioka and Yoshida 1994] 1) It is guaranteed that wavelet networks can su#ciently approximate any functions in the Hilbert space L 2 (R) 2) The internal expressions are readily understandable because of their multi resolutional structures. 3) It is not di#cult to add or ....
....and Krishnaprasad 1993] However, their method does not guarantee an optimal arrangement of windows because it uses only information of expansion coe#cients in network synthesis. In the present paper, we regard the arrangement of windows as a combinatorial optimization problem [Ueda, Kobayashi and Torioka 1997] Then we propose a new method to optimally arrange windows using genetic algorithms. Through computer experiments, it is verified that our method provide an optimal arrangement. 2 PRELIMINARIES In this section, the basics of wavelets is briefly introduced[Chui 1992] Throughout the present ....
[Article contains additional citation context not shown here]
Kobayashi and Torioka 1994. A Wavelet Neural Network for Function Approximation and Network Optimization, Vol.4, pp.505--510, Proceedings of ANNIE'94, AMSE Press.
Adaptive Self-Tuning Neuro Wavelet Network Controllers - Lekutai (1997) (Correct)
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K. Kobayashi and T. Torioka, "A Wavelet Neural Network for Function Approximation and Network Optimization," Intelligent Engineering Systems Through Artificial Neural Networks, Volume 4, C. H. Dagli, B. R. Fernandez, J. Ghosh, and R. T. Soundar Kumara, Eds., Proceedings of the Artificial Neural Networks in Engineering (ANNIE `94) Conference, pp. 505-10, 1994.
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