The Cauchy problem for fuzzy differential equations
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Neuro-Control Systems: Theory and Applications
Neuro-Control Systems: Theory and Applications
Choquet fuzzy integral based modeling of nonlinear system
Applied Soft Computing
Choquet fuzzy integral-based hierarchical networks for decision analysis
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
A new method for the control of discrete nonlinear dynamic systems using neural networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
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Design and learning of networks best suited for a particular application is a never-ending process. But this process is restricted due to problems like stability, plasticity, computation and memory consumption. In this paper, we try to overcome these problems by proposing two interval networks (INs), based on a simple feed-forward neural network (NN) and Choquet integral (CI). They have simple structures that reduce the problems of computation and memory consumption. The use of Lyapunov stability (LS) in combination with fuzzy difference (FD) based learning algorithm evolve the converging and diverging process which in turn assures the stability. FD gives a range of variation of parameters having the lower and the upper bounds within which the system is stable thus defining the plasticity. Effectiveness and applicability of the NN and CI based network models are investigated on several benchmark problems dealing with both identification and control.