The discrete wavelet neural network and its application in oscillographic chronopotentiometric determination

The structure and algorithm of the discrete wavelet neural network (DWNN) are described. The network is constructed by the error back propagation neural network using Morlet mother wavelet basic function as node activation function. The effect of wavelet base number, learning rate factor and momentum factor on prediction are discussed. The experimental results of the quantitative computation for the concentration of mono-component and multi-component in oscillographic chronopotentiometric determination (OCPD) show that number of epochs is less than 1000, the recovery is between 94.37% and 104.3%. Compared with standard back propagation neural network, DWNN has higher convergence rate and prediction accuracy.