Fuzzy deep wavelet neural network with hybrid learning algorithm: Application to electrical resistivity imaging inversion

Abstract

Electrical resistivity imaging (ERI) is a non-invasive imaging technique for measuring resistivity, and the inversion problem of ERI is non-linear and non-convex. Traditional fuzzy neural network based on gradient descent is known to be inept for its low accuracy and does not ensure global convergence. In order to solve above problems, we present a fuzzy deep wavelet neural network (FDWNN) inversion method trained by an accelerated hybrid learning algorithm to invert resistivity data of ERI. Firstly, a novel FDWNN model, which integrates the fuzzy clustering-based premise part with the deep WNN-based consequent part, is applied to improve the prediction accuracy and enhance the interpretability of ERI inversion. Secondly, an adaptive shuffled frog leaping algorithm (ASFLA) is introduced to balance the exploration and exploitation during the search process intelligently. In the proposed ASFLA, an adaptive mutation rule is applied to improve the local search and a differential leaping strategy is presented to enhance the global search. Finally, an accelerated hybrid learning algorithm integrating the ASFLA and a weight decay backpropagation (wdBP) method is designed, which keeps the advantages of the SFLA in finding global optimal values, while speeds up the convergence and improves the generalization through wdBP simultaneously. Moreover, five experiments are introduced to evaluate the feasibility and applicability of the FDWNN algorithm by comparison with other contenders.