Composite function wavelet neural networks with extreme learning machine

A new structure of wavelet neural networks (WNN) with extreme learning machine (ELM) is introduced in this paper. In the proposed wavelet neural networks, composite functions are applied at the hidden nodes and the learning is done using ELM. The input information is first processed by wavelet functions and then passed through a type of bounded nonconstant piecewise continuous activation functions g:R→Rg:R→R. A selection method that takes into account the domain of input space where the wavelets are not zero is used to initialize the translation and dilation parameters. The formed wavelet neural network is then trained with the computationally efficient ELM algorithm. Experimental results on the regression of some nonlinear functions and real-world data, the prediction of a chaotic signal and classifications on serval benchmark real-world data sets show that the proposed neural networks can achieve better performances in most cases than some relevant neural networks and learn much faster than neural networks training with the traditional back-propagation (BP) algorithm.