Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
A neuro-fuzzy system for ash property prediction
Second international workshop on Intelligent systems design and application
A neuro-fuzzy based forecasting approach for rush order control applications
Expert Systems with Applications: An International Journal
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
An Analytical Adaptive Single-Neuron Compensation Control Law for Nonlinear Process
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Expert Systems with Applications: An International Journal
Multi-objective optimization of TSK fuzzy models
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy neural network-based adaptive single neuron controller
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
On the stability of interval type-2 TSK fuzzy logic control systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
An improved Takagi-Sugeno fuzzy model with multidimensional fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
A GMDH-based fuzzy modeling approach for constructing TS model
Fuzzy Sets and Systems
International Journal of Hybrid Intelligent Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.01 |
This paper presents a fuzzy logic approach to complex systems modeling that is based on fuzzy discretization technique. As compared with other modeling methods (both statistical and fuzzy), the proposed approach has the advantages of simplicity, flexibility, and high accuracy. Further, it is easy to use and may be handled by an automatic procedure. Numerical examples are provided to illustrate the performance of the proposed approach