Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks -- A Novel Learning Algorithm and a Comparative Study

TL;DR

This paper introduces a novel sliding mode-based learning algorithm for type-2 fuzzy neural networks that enhances convergence speed and robustness, especially in noisy environments, and is easier to implement than existing methods.

Contribution

It develops a fully sliding mode parameter update algorithm for both premise and consequent parts of type-2 fuzzy neural networks, with adaptive learning rates and proven stability.

Findings

Faster convergence compared to gradient and swarm intelligence methods

Robust performance under noisy conditions

Simpler implementation due to closed-form updates

Abstract

In order to achieve faster and more robust convergence (especially under noisy working environments), a sliding mode theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks in this paper. Differently from recent studies, where sliding mode control theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully sliding mode parameter update rules for both the premise and consequent parts of the type-2 fuzzy neural networks. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.