The drawback of the backpropagation (BP) algorithm is slow training and easily convergence to the local minimum and suffers from saturation training. To overcome those problems, we created a new dynamic function for ...
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In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonome...
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In this paper, the performance comparison of various types of functional link neural networks (FLNNs) has been done for the nonlinear system identification. The FLNNs being compared in the present study are: trigonometry FLNN, Legendre FLNN (LeFLNN), Chebyshev FLNN, power series FLNN (PSFLNN) and Hermite FLNN. The recursive weights adjustment equations are derived using the combination of Lyapunov stability criterion and dynamic back propagation algorithm. In the simulation study, a total of three nonlinear systems (both static and dynamic systems) are considered for testing and comparing the approximation ability and computational complexity of the above-mentioned FLNNs. From the simulation results, it is observed that the LeFLNN has given better approximation accuracy and PSFLNN offered least computational load as compared to the rest models.
This study deals with design of an adaptive output feedback tracking controller for a class of non-linear systems with unknown fixed control direction. By using neural networks and deriving adaptive rules based on the...
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This study deals with design of an adaptive output feedback tracking controller for a class of non-linear systems with unknown fixed control direction. By using neural networks and deriving adaptive rules based on the steepest descent algorithm, the authors present a stable output feedback control scheme, which is applicable to a wide class of unknown complicated non-linear systems. Therefore an approach based on the dynamic back propagation algorithm is proposed to develop the adaption laws for systems with more general model structure. Using Lyapunov's direct method, uniformly ultimately boundedness of all signals of the closed-loop system is also ensured. Moreover, it is shown that the bounds on the tracking errors depend on the designing parameters. Hence, an arbitrarily small tracking error can be achieved by adjusting the parameters properly. Finally, simulation results performed on a non-affine uncertain non-linear system having internal dynamics are given to demonstrate the effectiveness of the proposed scheme and the theoretical discussions.
In this paper, a type of chaotic recurrent fuzzy neural network (CRFNN) model is proposed. The CRFNN model add chaotic map in the membership function layer of a RFNN. A generalized dynamic back propagation algorithm (...
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ISBN:
(纸本)9781424408276
In this paper, a type of chaotic recurrent fuzzy neural network (CRFNN) model is proposed. The CRFNN model add chaotic map in the membership function layer of a RFNN. A generalized dynamic back propagation algorithm (DBP) is developed to automatically construct the CRFNN. To guarantee the convergence by Lyapunov function, the online learning rate adjusting range is given. Simulation results of identifying chaotic system show that, CRFNN has better performance than normal method and the adaptive learning rate could improve efficiency and decrease approximation errors.
With the China's energy conservation and emissions reduction and energy structure adjustment working gradually thorough,the pumped storage power station has become an integral part of the *** has a crucial effect ...
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With the China's energy conservation and emissions reduction and energy structure adjustment working gradually thorough,the pumped storage power station has become an integral part of the *** has a crucial effect to the stable,safe and reliable operation of the power *** to the characteristics of the pumped storage units and the problems existing in the operation,using dynamic neural network method to accurately identify the dynamic characteristics of pumped storage unit in this *** becomes easy to realize the fuzzy inference function due to the introduction of product *** network is characterized by fewer parameters,faster convergence rate and the strong *** simulation results also verify the effectiveness and accuracy of the proposed fuzzy neural network.
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