Journal of the Korean Institute of Telematics and Electronics B (전자공학회논문지B)
- Volume 29B Issue 1
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- Pages.94-101
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- 1992
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- 1016-135X(pISSN)
Improvement of Learning Capabilities in Multilayer Perceptron by Progressively Enlarging the Learning Domain
점진적 학습영역 확장에 의한 다층인식자의 학습능력 향상
Abstract
The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.
Keywords