The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models

Abstract

Hybrid systems of the fuzzy logic and neural networks, are widely spread in real world problems with high effectiveness and versatility for different kinds of applications. The state description of unknown plant by using mathematical models, sometimes, is difficult to obtain. The fuzzy logic systems with their ability of tackling imprecise knowledges, and neural networks with their advantages of establishing a relationship between the inputs and the outputs of the system, are represented as qualified tools for systems of unknown plant. Furthermore, the hybrid systems which utilize the features of the fuzzy logic and Neural networks has been employed for better characteristics. Whilst, there are several different architectures of the neuro-fuzzy system proposed in literature, this article come out to highlight the common known architectures of how these techniques fuse together to build an enhanced system that can complement the lack of each method individually and improve the system performance over all.

Hybrid FS–WNN approximator in indirect adaptive control of uncertain non‐linear MIMO systems

“…The fusion of FSs with NNs or WNNs in a single network is an effective method to overcome their individual limitations. However, the fused network of Chairez [15] and the FWNN of Tsai et al [16] are expansions of ANFIS [21,22], where different training algorithms are used to adjust the network structure and parameters. Therefore, these fused approximators inherit the difficulties of ANFIS.…”