Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
The nature of statistical learning theory
The nature of statistical learning theory
From regularization operators to support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Mathematics and Computers in Simulation
Neural Computation
Face recognition using point symmetry distance-based RBF network
Applied Soft Computing
On-line system identification of complex systems using Chebyshev neural networks
Applied Soft Computing
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Corrective action planning using RBF neural network
Applied Soft Computing
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid approach of selecting hyperparameters of support vector machine for regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Intelligent control of a constant turning force system with fixed metal removal rate
Applied Soft Computing
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This paper demonstrates that radial basis function networks (RBFNs) with support vector regression (SVR) and annealing robust learning algorithm (ARLA) can be used effectively for the identification of the nonlinear dynamic systems with outliers. When the RBFNs are used for the identification of the nonlinear dynamic system, the number of hidden nodes, the initial parameters of the kernel, and the initial weights of the network must be determined first, a SVR approach is proposed to solve the initial problem of RBFNs. That is, the SVR uses the quadratic programming optimization to determine the initial structure of the RBFNs. Besides, the new cost function for the system identification with outliers is also proposed. That is, the proposed annealing robust radial basis function networks (ARRBFNs) are trained by the ARLA, which uses the annealing concept in the cost function of the robust back-propagation learning algorithm, can overcome the error measurement caused by the outliers. Simulation results show the superiority of the proposed method with different SVR.