This paper presents a new method for training multi-layer perceptron networks called DMP1 (Dynamic Multilayer 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 genetic algorithm. The method is capable of handling real-valued 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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