Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be
designed either from expert knowledge or from data. For complex systems, FIS based on …

Abstract Fuzzy model identification is an effective tool for the approx-imation of uncertain
nonlinear systems on the basis of measured data. The identification of a fuzzy model using …

Nonlinear model-based predictive control (MBPC) in multi-input multi-output (MIMO) process
control is attractive for industry. However, two main problems need to be considered:(i) …

This paper addresses the optimization in fuzzy model predictive control. When the prediction
model is a nonlinear fuzzy model, nonconvex, time-consuming optimization is necessary …

This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi-
Sugeno (TS) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and …

Accurate estimation of the software effort and schedule affects the budget computation.
Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to …

This paper presents a new, supervised, hierarchical clustering algorithm (SUHICLUST) for
fuzzy model identification. The presented algorithm solves the problem of global model …

In this paper, we use Takagi-Sugeno (TS) technique to develop fuzzy models for two
nonlinear processes. They are the software effort estimation for a NASA software projects …

Adequate membrane bioreactor operation requires frequent evaluation of the membrane
state. A data-driven approach based on principal component analysis (PCA) and fuzzy …

In this paper a neuro-fuzzy modeling framework is proposed, which is devoted to discover
knowledge from data and represent it in the form of fuzzy rules. The core of the framework is …