Improving adaptive network fuzzy inference system with Levenberg-Marquardt algorithm

Abstract

The well-known adaptive neuro-fuzzy inference system (ANFIS) uses a combination of least-square estimation (LSE) and gradient descent back-propagation methods to model a training data set. In this paper, we show that the rate of convergence of ANFIS can be very much improved by using a combination of LSE and Levenberg-Marquardt algorithm (LMA). The improved ANFIS converges more closely and significantly more rapidly to the data. Detail explanation of the proposed ANFIS is presented, and its validity is verified via simulation.