Abstract
A novel algorithm for determining the optimal initial weights of multilayer perceptrons based on an independent component analysis is developed. The algorithm is able to initialize the hidden layer weights that extract the salient feature components from the input data. The initial output layer weights are evaluated in such a way that the output neurons are kept inside the active region. Real-world benchmark problems were used for validating the proposed algorithm. The simulation results indicate that the proposed algorithm gets a substantial reduction in the required training time as compared with the other well-known weight initialization schemes. The proposed weight initialization method is capable of speeding up the learning process of multilayer perceptrons effectively.
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