Title

Study Of Neural Network Disturbance Learning And Application In Robocup

Keywords

Machine learning; Neural networks; RoboCup

Abstract

To solve the problem of convergence to a local optimum in the multi-layer feedforward neural network, a new disturbance gradient algorithm is proposed. Through introducing random disturbance into the training process, the algorithm can avoid being trapped into the local optimum. The random disturbance obeys the Boltzmann distribution. The convergence of the algorithm to the global optimum is statistically guaranteed. The application of the algorithm in RoboCup, which is a complex multi-agent system, is discussed. Experiment results illustrate the learning efficiency and generalization ability of the proposed algorithm.

Publication Date

6-1-2007

Publication Title

High Technology Letters

Volume

13

Issue

2

Number of Pages

203-206

Document Type

Article

Personal Identifier

scopus

Socpus ID

34347215898 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34347215898

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