Efficient wavelet networks for function learning based on adaptive wavelet neuron selection


In this study, a novel four-layer architecture of wavelet network is proposed for function learning. Compared to conventional three-layer wavelet networks, the proposed one exploits adaptive wavelet neuron selection technique according to input information, so that the widespread structural redundancy is avoided. Meanwhile, it controls the scale of problem solution. Based on the proposed architecture, two wavelet networks including single-wavelet neural network and multiwavelet neural network are built and verified for function learning. The experimental results demonstrate that our models are remarkably superior to some of the well-established three-layer wavelet networks including Zhang's model and Pati's model in terms of both speed and accuracy. Compared with Huang's real-time neural network, the proposed models have significantly better accuracy with basically similar speed.

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