Principles of interactive computer graphics (2nd ed.)
Principles of interactive computer graphics (2nd ed.)
System identification: theory for the user
System identification: theory for the user
Practical numerical algorithms for chaotic systems
Practical numerical algorithms for chaotic systems
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Designing fuzzy controllers by rapid learning
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Swarm intelligence
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Journal of Global Optimization
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
New fuzzy wavelet neural networks for system identification and control
Applied Soft Computing
Fuzzy logic models for ranking process effects
IEEE Transactions on Fuzzy Systems
Nonlinear system modeling via knot-optimizing B-spline networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Oscillatory neural networks for robotic yo-yo control
IEEE Transactions on Neural Networks
On the construction and training of reformulated radial basis function neural networks
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A 2-Opt based differential evolution for global optimization
Applied Soft Computing
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B-Spline Neural Network (BSNN), a type of basis function neural network, is trained by gradient-based methods which may fall into local minima during the learning procedure. To overcome the limitations encountered by gradient-based optimization methods, we propose differential evolution (DE) - an evolutionary computation methodology - which can provide a stochastic search to adjust the control points of a BSNN. In this paper, we propose six DE approaches using chaotic sequences based on logistic mapping to train a BSNN. Chaos describes the complex behavior of a nonlinear deterministic system. The application of chaotic sequences instead of random sequences in DE is a powerful strategy to diversify the DE population and improve the DE's performance in preventing premature convergence to local minima. The numerical results presented here indicate that chaotic DE was effective for building a good BSNN model for the nonlinear identification of an experimental nonlinear yo-yo motion control system.