Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

Abstract

This paper deals with the synchronization of fuzzy neural networks (FNNs) with time-varying delays. FNNs are more complicated form of neural networks incorporated with fuzzy logics, which provide more powerful performances. Especially, the problem of delayed FNNs's synchronization is of importance in the existence of the network communication. For the synchronization of FNNs with time-varying delays, a novel form of control structure is proposed employing affinely transformed membership functions with memory element. In accordance with affine memory control, appropriate Lyapunov-Krasovskii functional is chosen to design control gain, guaranteeing stability of the systems with delays. Exploiting the more general type of control attributed by affine transformation and memory-type, a novel criterion is derived in forms of linear matrix inequalities (LMIs). As a results, the effectiveness of the proposed control is shown through numerical examples by comparisons with others.

Toward enhancement of deep learning techniques using fuzzy logic: a survey

“…By employing fuzzy settings or fuzzy logic to determine model parameters, fuzzy systems may be employed as an important element of deep learning techniques [24]. This innovative DNFS method has demonstrated the ability to generate fuzzy rules even without assistance of a human expert, therefore resolving the DNN's "black-box" issue [25]. Moreover, given the same degree of abstraction, the DNFS method outperforms DNN [26], [27].…”