A Provably Convergent Dynamic Training Method for Multi-layer Perceptron Networks
نویسندگان
چکیده
This paper presents a new method for training multi-layer perceptron networks called DMP1 (Dynamic Multi-layer Perceptron 1). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The individual nodes of the network are trained using a gentetic algorithm. The method is capable of handling realvalued inputs and a proof is given concerning its convergence properties of the basic model. Simulation results show that DMP1 performs favorably in comparison with other learning algorithms.
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