This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptot...

This paper summarizes work undertaken in the area of modeling Shape Memory Alloy (SMA) and airfoil hysteresis using a Sugeno-type fuzzy modeling approach based on subtractive clustering. Two alternative approaches to develop a fuzzy model for hysteresis are proposed and evaluated. The first consists in building a mirror image of the lower curve in order to model both curves concurrently and the...

To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with exist...

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune ...

One of the emerging areas in the field of next generation wireless network is the Cognitive Radio Network (CRN). The traditional spectrum access approach leads to vast underutilization of the radio spectrum assigned to primary (licensed) users. The dynamic spectrum access is the most suitable approach to achieve finest spectrum utilization. The dynamic spectrum access technique allows the secon...

In this paper a novel neural fuzzy inference network (NFIN) it is proposed. The NFIN represent a modified Takagi-Sugeno-Kang (TSK) type fuzzy rule based model with neural network learning ability. The rules in the NFIN are created and adapted in an on-line learning algorithm. The structure learning together with the parameter learning forms the learning algorithms for the neural fuzzy network. ...

A fuzzy Elman neural network (FENN) is proposed to identify and simulate nonlinear dynamic systems. Each of all the fuzzy rules used in FENN has a linear state-space equation as its consequence and the network, by use of firing strengths of input variables, combines these Takagi-Sugeno type rules to represent the modeled nonlinear system. The context nodes in FENN are used to perform temporal r...

The single input rule modules (SIRMs) based type-1 fuzzy inference systems (SIRM-T1FISs), which assign each input item with a rule module, can greatly reduce the number of fuzzy rules and have found lots of applications. This paper extends SIRMT1FISs to the interval type-2 case to achieve better performance. Furthermore, the properties of the SIRMs based interval type-2 fuzzy inference systems ...

In this paper, we review two of the most well-known citation indexes and establish their connections with the Choquet and Sugeno integrals. In particular, we show that the recently established -index is a particular case of the Sugeno integral, and that the number of citations corresponds to the Choquet integral. In both cases, they use the same fuzzy measure. The results presented here permit ...

Two fuzzy models for real estate appraisal, i.e. Mamdani-type and Takagi-Sugeno-Kang-type have been built with the aid of experts. Both models comprised 7 input variables referring to main attributes of a property being appraised. In order to determine the rule bases for both models an evolutionary algorithm has been applied. The experiments conducted using MATLAB software revealed that models ...