Neural Networks
Sugeno type controllers are universal controllers
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Approximation of dynamical systems by continuous-time recurrent approximate identity neural networks
Neural, Parallel & Scientific Computations
Universal approximation in p-mean by neural networks
Neural Networks
Reinforcement Learning Using the Stochastic Fuzzy Min–Max Neural Network
Neural Processing Letters
Handbook of Neural Computation
Handbook of Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A survey of recent advances in fuzzy logic in telecommunications networks and new challenges
IEEE Transactions on Fuzzy Systems
General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
On the approximation of stochastic processes by approximate identity neural networks
IEEE Transactions on Neural Networks
An adaptive controller design for uncertain stochastic systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Stabilization for T-S model based uncertain stochastic systems
Information Sciences: an International Journal
Information Sciences: an International Journal
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Fuzzy systems can provide us with universal approximation models of deterministic input-output relationships, but in the stochastic environment few achievements related to the subject have so far achieved. In the paper a novel stochastic Takagi-Sugeno (T-S) fuzzy system is introduced to represent approximately existing randomness in many real-world systems. By recapitulating the general architecture of the stochastic T-S fuzzy rule-based system, we analyze systematically approximating capability of the stochastic system to a class of stochastic processes. By the canonical representation of the stochastic processes, the stochastic fuzzy system is capable of with arbitrary accuracy providing the approximation to the stochastic processes in mean square sense. Finally, an efficient algorithm for the stochastic T-S fuzzy system is developed. A simulation example demonstrates how a stochastic T-S fuzzy system can be constructed to realize the given stochastic process, approximately.