Distributed wavelet neural networks

Abstract

Utilizing the wavelet theory, the wavelet coefficients with respect to translation and scaling factors can be obtained through the iteration of neural network, which effectively solves the window immobility problem of short time Fourier transform. Notice that, the centralized wavelet neural network is weak in the representation of signal integrity as localized information is lost in the case of the signal properties are large span time-domain and high frequency. To solve this problem, the learning algorithm of distributed wavelet neural networks (DWNNs) based on domain segment is proposed in this paper, where the time frequency characteristic of objective function is set as a constraint condition, while the time width, frequency, center of time domain and band center of the wavelet are employed to determine the translation and scaling parameters. Especially, wavelet networks are distributed into several orthogonal subnet by partitioning the information of input data, and the complexity of calculating for each nodes is thus decreased. Moreover, simulations are presented to demonstrate the advantages and efficiency of the proposed DWNNs.

Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production

“…Predictive performance of neural network model WNN (Wavelet neural network) 32,33 and ENN (Elman neural network) [34][35][36] are chosen for experimental comparison to evaluate efficacy of LSTM. Table 2 shows parameter settings of different neural networks.…”