A course in fuzzy systems and control
A course in fuzzy systems and control
Designing a fuzzy model by adaptive macroevolution genetic algorithms
Fuzzy Sets and Systems
Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms
Applied Intelligence
Evolutionary Learning of Modular Neural Networks withGenetic Programming
Applied Intelligence
Design of robust fuzzy-model-based controller with sliding mode control for SISO nonlinear systems
Fuzzy Sets and Systems - Fuzzy control
A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology
Fuzzy Sets and Systems - Theme: Learning and modeling
Modeling of hierarchical fuzzy systems
Fuzzy Sets and Systems - Theme: Learning and modeling
A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection
Applied Intelligence
Computational Intelligence: Principles, Techniques and Applications
Computational Intelligence: Principles, Techniques and Applications
Designing a hierarchical fuzzy logic controller using the differential evolution approach
Applied Soft Computing
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
A hierarchical fuzzy system with high input dimensions for forecasting foreign exchange rates
International Journal of Artificial Intelligence and Soft Computing
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
On the construction of hierarchical fuzzy systems models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fuzzy adaptive variable structure controller with applications torobot manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robustness design of nonlinear dynamic systems via fuzzy linear control
IEEE Transactions on Fuzzy Systems
Fuzzy model based adaptive control for a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
Stable model reference adaptive fuzzy control of a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms
IEEE Transactions on Fuzzy Systems
Fuzzy control of inverted pendulum and concept of stability using Java application
Mathematical and Computer Modelling: An International Journal
Formal approach for reengineering fuzzy XML in fuzzy object-oriented databases
Applied Intelligence
Storing and querying fuzzy XML data in relational databases
Applied Intelligence
Formal transformation from fuzzy object-oriented databases to fuzzy XML
Applied Intelligence
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An evolutionary algorithm based approach for selection of topologies in hierarchical fuzzy systems (HFS) is presented. Coupling fuzzy system with evolutionary algorithm provides a solution to the automated acquisition of the fuzzy rule base. It is difficult to study the problem of hierarchical decomposition for a large class of fuzzy systems but it is possible to analyse such architectures on the example of a particular fuzzy system, such as inverted pendulum. Topology of the HFS must be selected according to the physical properties of the dynamical system under consideration. Different HFS topologies for an inverted pendulum system are investigated and analysed to address the problem of how input configuration in multi-layered structure affects the controller performance. The experiments are conducted to test controller performance for different topologies of the hierarchical fuzzy system. The impact of different topologies on control process is discussed. The results from the case study of inverted pendulum can be extended to other dynamical systems.