New random search method for Neural Networks learning - Random Search with Variable Search Length (Ras Val)

Kotaro Hirasawa, Kazuyuki Togo, Junichi Murata, Masanao Ohbayashi, Ning Shao, Jinglu Hu

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

In this paper, a new random search method Ras Val for Neural Networks (NN) learning is proposed. RasVal (Random Search with Variable Search Length) is a kind of random search and it can find a global minimum instead of a local minimum using the capability of intensified and diversified searches. The main different point of RasVal from commonly used Random Search Methods (RSM) is that the shape of the probability density function for random searching can be adjusted based on the information of success or failure of the search. First, RasVal is described and after that, performance between RasVal, Back Propagation Method (BP) and Back Propagation Method with momentum (Mom. BP) are compared. The performance is evaluated by the simulations which include both static and dynamic Neural Networks (NN) learning problems. In the simulations, NN is trained to realize nonlinear functions and to control a nonlinear crane system by using RasVal, BP and Mom. BP. Simulation results show that Ras Val is superior or nearly equal to BP and Mom. BP because of the ability of intensification and diversification of the search.

Original languageEnglish
Pages1602-1607
Number of pages6
Publication statusPublished - 1998 Jan 1
Externally publishedYes
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: 1998 May 41998 May 9

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period98/5/498/5/9

ASJC Scopus subject areas

  • Software

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