This book introduces the novel concept of a fuzzy network. In particular, it describes further
developments of some results from its predecessor book on Complexity Management in …

In this paper, a new method for nonlinear system identification via extreme learning machine
neural network based Hammerstein model (ELM-Hammerstein) is proposed. The ELM …

In this study, auto regressive with exogenous input (ARX) modeling is improved with fuzzy
functions concept (FF-ARX). Fuzzy function with least squares estimation (FF-LSE) method …

This paper proposes an enhanced discriminability recurrent fuzzy neural network for
temporal classification problems. To consider classification problems, the most important …

This paper presents a Wiener-type recurrent neural network with a systematic identification
algorithm and a control strategy for the identification and control of unknown dynamic …

In this study, a Wiener-type neural network (WNN) is derived for identification and control of
single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified …

Based on polynomial interpolation and approximation theory, a special feed-forward neural
network using power activation functions is constructed in this paper. The neural model …

In this paper, a brain-inspired fuzzy emotional neural network (FUZZ-ENN) is proposed for
uncertainty prediction tasks in real world applications. In the proposed FUZZ-ENN …

This brief presents a structure for black-box identification based on continuous-time
recurrent neurofuzzy networks for a class of dynamic nonlinear systems. The proposed …

In this paper, a new Wiener model is presented for identification and control of single-input
single-output (SISO) nonlinear systems. The proposed Wiener model consists of a linear …