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Training Multilayer Neural Network by Global Chaos Optimization Algorithms

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2 Author(s)
Khoa, T.Q.D. ; Nagaoka Univ. of Technol., Niigata ; Nakagawa, Masahiro

Fractals and chaos are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 procedures. The first one is Breadth-first search and the second one is Depth-first search. The proposal algorithm is examined by 2 functions, the Camel function and the Schaffer function. Furthermore, the proposal algorithm is applied to optimize training Multilayer Neural Networks.

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007