The Effect of Decision Surface Fitness on Dynamic Multi-layer Perceptron Networks (DMP1)
نویسندگان
چکیده
The DMP1 (Dynamic Multi-layer Perceptron 1) network training 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. This paper introduces the DMP1 method, and compares the preformance of DMP1 when using the standard delta rule training method for training individual nodes against the performance of DMP1 when using a genetic algorithm for training. While the basic model does not require the use of a genetic algorithm for training individual nodes, the results show that the convergence properties of DMP1 are enhanced by the use of a genetic algorithm with an appropriate fitness function.
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A Provably Convergent Dynamic Training Method for Multi-layer Perceptron Networks
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 real...
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