Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Neural network model-based automotive engine air/fuel ratio control and robustness evaluation
Engineering Applications of Artificial Intelligence
A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation
Engineering Applications of Artificial Intelligence
Signature verification (SV) toolbox: Application of PSO-NN
Engineering Applications of Artificial Intelligence
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
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Harmonic estimation is the main process in active filters for harmonic reduction. A hybrid Adaptive Neural Network-Particle Swarm Optimization (ANN-PSO) algorithm is being proposed for harmonic isolation. Originally Fourier Transformation is used to analyze a distorted wave. In order to improve the convergence rate and processing speed an Adaptive Neural Network Algorithm called Adaline has then been used. A further improvement has been provided to reduce the error and increase the fineness of harmonic isolation by combining PSO algorithm with Adaline algorithm. The inertia weight factor of PSO is combined along with the weight factor of Adaline and trained in Neural Network environment for better results. ANN-PSO provides uniform convergence with the convergence rate comparable that of Adaline algorithm. The proposed ANN-PSO algorithm is implemented on an FPGA. To validate the performance of ANN-PSO; results are compared with Adaline algorithm and presented herein.