An Analysis Of PSO Hybrid Algorithms For Feed-Forward Neural Networks Training
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Adaptive nonlinear system identification using minimal radial basis function neural networks
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
A Novel Recurrent Generalized Congruence Neural Network for Dynamical System Identification
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Nonlinear System Identification Using Dynamic Neural Networks Based on Genetic Algorithm
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
A Dynamic Mutation PSO algorithm and its Application in the Neural Networks
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A direct adaptive neural-network control for unknown nonlinear systems and its application
IEEE Transactions on Neural Networks
An efficient parameterization of dynamic neural networks for nonlinear system identification
IEEE Transactions on Neural Networks
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Evolving multilayer feedforward neural network using adaptive particle swarm algorithm
International Journal of Hybrid Intelligent Systems
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In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified Particle Swarm Optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and Logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.