Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Back-propagation gradient descent method was performed to train the ANFIS system. The performance of the...

A semi-mechanistic fuzzy modeling technique is proposed to obtain compact and transparent process models based on small data-sets. Semi-mechanistic models are hybrid models that consist of a white box structure based on mechanistic relationships and black-box substructures to model less defined parts. First, it is shown that certain type of white-box models can be efficiently incorporated into ...

The complex hybrid and nonlinear nature of many processes that are met in practice causes problems with both structure modelling and parameter identification; therefore, obtaining a model that is suitable for MPC is often a difficult task. The basic idea of this paper is to present an identification method for a piecewise affine (PWA) model based on a fuzzy clustering algorithm. First we introd...

The maximization of the performance of the most if not all the fuzzy identification techniques is usually expressed in terms of the generalization performance of the derived neuro-fuzzy construction. Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance. However, these SVFI rules usually lack...

This paper deals with transforming Support vector regression (SVR) models into fuzzy systems (FIS). It is highlighted that trained support vector based models can be used for the construction of fuzzy rule-based regression models. However, the transformed support vector model does not automatically result in an interpretable fuzzy model. Training of a support vector model results a complex rule...

Gibbs distribution is used to represent fuzzy codebooks of individual speakers. The method of fuzzy evolutionary programming is employed to create the fuzzy codebooks and also to train hidden Markov models of speakers. This method increases the chance of attaining global maxima in Baum-Welch algorithm for hidden Markov model re-estimation. The experiments show the results of speaker identificat...

This paper investigates the technique of the modeling and identification a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model, all which limit their usefulness during dealing with dynamic nonlinear industrial processes. To ...

93 AIJ Modeling, Identification, Simulation and Control, Vol 48, No. 2, Fall 2016 Please cite this article using: Kazemi, A., Talebi, A., and Oroojeni Mohammad-Javad, M., 2016. “Analysis of Critical Paths in a Project Network with Random Fuzzy Activity Times”. Amirkabir International Journal of Modeling, Identification, Simulation and Control, 48(2), pp. 93–101. DOI: 10.22060/miscj.2016.831 URL...

Abstract: This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine. Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upp...

We discuss several fuzzy models to approximate friction and other disturbances in mechatronic systems, especially linear and rotarional electrical drives. Some methods of experimental identification of disturbance forces are presented. We consider several fuzzy models to compromise between model accuracy and complexity. Fuzzy model is used in an adaptive control loop. Several adaptive control a...