A new efficient computational technique for training of multilayer feedforward neural networks is proposed. The proposed algorithm consists of two learning phases. The first phase is a local search which implements gradient descent, and the second phase is a direct search scheme which implements dynamic tunneling in weight space avoiding the local trap and thereby generates the point of next descent. The repeated application of these two phases alternately forms a new training procedure which results in a global minimum point from any arbitrary initial choice in the weight space. The simulation results are provided for five test examples to demonstrate the efficiency of the proposed method which overcomes the problem of initialization and local minimum point in multilayer perceptrons.
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