Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
Composite Function Wavelet Neural Networks with Differential Evolution and Extreme Learning Machine
Neural Processing Letters
Engineering Applications of Artificial Intelligence
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This paper proposes a new nonlinear system identification scheme using differential evolution (DE), neural network and Levenberg Marquardt algorithm (LM). Here, DE and LM in a combined framework are used to train a neural network for achieving better convergence of neural network weight optimization. A number of examples including a practical case-study have been considered for implementation of different system identification methods namely, only NN, DE+NN and DE+LM+NN. After, a series of simulation studies of these methods on the different nonlinear systems it has been confirmed that the proposed DE and LM trained NN approach to nonlinear system identification has yielded better identification results in terms of time of convergence and less identification error.