This paper addresses the problem of predicting the sales by developing two sales forecasting models based on multi-layered perceptron (MLP) and radial basis function network (RBFN). The performance of both these model...
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In this paper, a novel dynamic recurrent functional link neural network (DRFLNN) is proposed for the identification of unknown dynamics of the nonlinear systems. The proposed structure contains a self-feedback loop(s)...
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In this paper, a novel dynamic recurrent functional link neural network (DRFLNN) is proposed for the identification of unknown dynamics of the nonlinear systems. The proposed structure contains a self-feedback loop(s) as well as the adjustable weighted feed-through of the input signals to the output neuron(s). A learning algorithm is developed using the combination of Lyapunov stability and dynamicback-propagation method and is applied to derive the stable parameter adjustment equations. The performance evaluation of the proposed DRFLNN model is done by comparing it with the multi-layer perceptron (consisting of a single hidden layer), radial basis function network, Elman recurrent neural network (ERNN), nonlinear auto-regressive moving average, and the conventional functional link neural network. Three benchmark systems have been used on which all these models are applied. From the results, it is found that ERNN provided better prediction accuracy as compared to the remaining models and the second-best accuracy is obtained from the proposed model. Further, the ERNN model is more complex and offers more parameters to be tuned as compared to the DRFLNN model. Thus, the training of the ERNN model is quite difficult as compared to the DRFLNN.
Traditional fuzzy neural network is a static map, not suitable for induction motor state identification. To improve the accuracy of system identification, a dynamic TS recurrent fuzzy neural network observer was propo...
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ISBN:
(纸本)9780769549323;9781467356527
Traditional fuzzy neural network is a static map, not suitable for induction motor state identification. To improve the accuracy of system identification, a dynamic TS recurrent fuzzy neural network observer was proposed. The dynamic back-propagation algorithm was derived from dynamic recurrent neural network observer model, which using Lyapunov Theorem to prove that the observer with global convergence. Simulation results show that: Because dynamic TS recurrent fuzzy neural network observer use the current data and historical data for state recognition at the same time, it has wonderful performance in the recognition accuracy and stability and better convergence than the traditional fuzzy neural network observer.
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