Online Evolving TSK fuzzy identification

Structure Identification of TSK-Fuzzy Systems using Genetic Programming

Identification of evolving fuzzy rule-based models

An approach to identification of evolving fuzzy rule-based (eR) models is proposed in this paper. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rule...

Stochastic Stability of TSK Fuzzy Model

in this paper, a theorem is derived for the existence of a common quadratic Lyapunov function for stability analysis of TSK fuzzy model. This paper proposed a new method based on stochastic stability. In this paper we study the stochastic stability properties of certain TSK Fuzzy system has been studied.

Identifying Rule-Based TSK Fuzzy Models

ABSTRACT: This article presents a rule-based fuzzy model for the identification of nonlinear MISO (multiple input, single output) systems. The presented method of fuzzy modeling consists of two parts: (1) structure modeling, i.e., the determination of the number of rules and input variables involved respectively, and (2) parameter optimization, i.e., the optimization of the location and form of...

An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

A new neuro-fuzzy system’s architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers’ location of membership functions and the receptive field’s parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the hel...