Fuzzy Sets and Systems - Fuzzy systems
Learning Capability: Classical RBF Network vs. SVM with Gaussian Kernel
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Expert Systems with Applications: An International Journal
Generalized multiscale radial basis function networks
Neural Networks
An Empirical Comparison of Training Algorithms for Radial Basis Functions
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Model-free adaptive control design using evolutionary-neural compensator
Expert Systems with Applications: An International Journal
Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
International Journal of Bio-Inspired Computation
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Fast forward RBF network construction based on particle swarm optimization
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
A two-stage algorithm for identification of nonlinear dynamic systems
Automatica (Journal of IFAC)
Letters: Random optimized geometric ensembles
Neurocomputing
Fast image classification algorithms based on random weights networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Sparse algorithms of Random Weight Networks and applications
Expert Systems with Applications: An International Journal
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This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large