Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Heuristic learning in recurrent neural fuzzy networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - SBRN'02
Neural-network-based robust adaptive control for a class of nonlinear systems
Neural Computing and Applications
Adaptive neural network control of nonlinear systems by state andoutput feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
An adaptive tracking controller using neural networks for a class of nonlinear systems
IEEE Transactions on Neural Networks
Comparison of entity with fuzzy data types in fuzzy object-oriented databases
Integrated Computer-Aided Engineering
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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An intelligent control methodology for a long-term ecological systems is developed in this paper. This intelligent control methodology is called as robust recurrent fuzzy neural network control RRFNNC. This control methodology is used to deal with multi-biomass ecological system which is an uncertain nonlinear system subject to unpredictable but bounded disturbances. This RRFNNC system is comprised of a recurrent fuzzy neural network RFNN controller and a robust controller. The RFNN controller is used to approximate an ideal controller; and the robust controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. The proposed RRFNNC system is applied to keep the multi-biomasses of ecological system within a stay small neighborhood of the unique nontrivial optimal equilibrium state of the undisturbed exploited ecosystem. For the simulation results of accumulative yield of harvest, more harvest can be obtained by applying the proposed RRFNNC system when compared with state feedback control.