In this paper, a TSK-type compensatory neural fuzzy network (TCNFN) for classification applications is proposed. The TCNFN model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 3 of the TCNFN model contains adaptive compensatory fuzzy operations, which make fuzzy logic systems more adaptive and effective. Furthermore, a self-adaptive learning algorithm,...

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...

In this paper, the robust stability analysis of a fuzzy vehicle lateral system with perturbed parameters is presented. Firstly, the fuzzy controller can be linearized by utilizing the describing function method with experiments. After the describing function is obtained, the stability analysis of the vehicle lateral control system with the variations of velocity and friction is then carried out...

This paper presents the stability and robustness analyses of uncertain nonlinear control systems. TO proceed with the analysis, the uncertain nonlinear system is represented by a fuzzy model with uncertainties. Based on this model, under a single-grid-point (SGP) approach, a fuzzy controller is designed to close the feedback loop. A simple stability criterion is derived by estimating the norm o...

In most control and engineering applications, the use of fuzzy system as a way to improve the humtzn-computer interaction has becoming popular. This paper reports on the use of fuzzy system in mineral processing specifically in determining the parameter d50c of hydrocyclone. However, wit,h the inputoutput data provided to build the fuzzy rule base, it normally results in a sparse fuzzy rule bas...

In this paper, a novel fault-tolerant control scheme is proposed for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems based on online fuzzy clustering and identification. The unknown nonlinear function of the system is identified online using an evolving Takagi-Sugeno (T-S) fuzzy model and the fault-tolerant control law is designed based on the identified fuzzy model...

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation h...

this paper presents a gain scheduled autopilot for pitch channel of a flying vehicle. the selected method is based on polynomial fuzzy systems. the method does not involve linearization about operating point. first the polynomial fuzzy model of pitch channel of the flight body is derived. next, using polynomial fuzzy system methodology the controller is design such that the outputs of the nonli...

in this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. as an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...