Recent fuzzy and neural control papers and MATLAB toolboxes

Dear Colleagues,
I would like to call your kind attention to the updated website of the Soft Computing Research Group at the University of Veszprem
(Hungary) /
You can download MATLAB Toolboxes:
- Fuzzy Clustering MATLAB Toolbox - Genetic Programming MATLAB Toolbox - Interactive Evolutionary Strategy (EASy) MATLAB Toolbox - Constrained Fuzzy Model Identification for the FMID Toolbox
independent MATLAB programs related to:
- Data mining * Fuzzy clustering based time-series segmentation * Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers * Fuzzy Modeling with Multidimensional Membership Functions: Grey-Box Identification and Control Design * Compact TS-Fuzzy Models through Clustering and OLS plus FIS Model Reduction * Inconsistency Analysis of Labeled Data * Star plots - MATLAB files for Graphical Representation of trace elements of clinkers
- Process control and monitoring * Feedback Linearizing Control Using Hybrid Neural Networks Identified by Sensitivity Approach * Incorporating Prior Knowledge in Cubic Spline Approximation - Application to the Identification of Reaction Kinetic Models * Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models - A Simple Fuzzy Classifier based on
manuscripts in PDF about
- fuzzy model based process control and monitoring - fuzzy clustering and classification - incorporation of a priori knowledge in the identification of fuzzy systems - block-oriented modelling of dynamical systems - fuzzy clustering and its applications to chemometrics - generic model control (GMC) based on hybrid models.
The related transparencies of the conference presentations and MATLAB program codes and data are also available.
Supporting MATLAB and Simulink files of the book:
Fuzzy Model Identification for Control Jnos Abonyi, University of Veszprm, Hungary January 2003 / 288 pp. / 132 ill. / Hardcover ISBN 0-8176-4238-2, Price: $74.95
are also available. This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice.
Your comments and suggestions are truly welcome.
Yours sincerely,
Janos Abonyi, Ph.D /
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