The nature of statistical learning theory
The nature of statistical learning theory
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Fast learning in networks of locally-tuned processing units
Neural Computation
Training RBF neural network via quantum-behaved particle swarm optimization
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experiments with repeating weighted boosting search for optimization signal processing applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid linear/nonlinear training algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Boid particle swarm optimisation
International Journal of Innovative Computing and Applications
Individual predicted integral-controlled particle swarm optimisation
International Journal of Innovative Computing and Applications
Nearest neighbor interaction PSO based on small-world model
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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 new stochastic algorithm to direct orbits of chaotic systems
International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
International Journal of Bio-Inspired Computation
Using group-decided Watts-Strogatz particle swarm optimisation to direct orbits of chaotic systems
International Journal of Wireless and Mobile Computing
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Dynamic packet fragmentation based on particle swarm optimised prediction
International Journal of Wireless and Mobile Computing
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A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.