Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IEEE Transactions on Signal Processing
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of Hammerstein nonlinear ARMAX systems
Automatica (Journal of IFAC)
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
A perceptron network for functional identification and control of nonlinear systems
IEEE Transactions on Neural Networks
Differential evolution-based nonlinear system modeling using a bilinear series model
Applied Soft Computing
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance.