Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Face recognition using point symmetry distance-based RBF network
Applied Soft Computing
BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
A novel method for prediction of protein interaction sites based on integrated RBF neural networks
Computers in Biology and Medicine
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The time series prediction of a practical power system is investigated in this paper. The radial basis function neural network (RBFNN) with a nonlinear time-varying evolution particle swarm optimization (NTVE-PSO) algorithm is developed. When training RBFNNs, the NTVE-PSO method is adopted to determine the optimal structure of the RBFNN to predict time series, in which the NTVE-PSO algorithm is a dynamically adaptive optimization approach using the nonlinear time-varying evolutionary functions for adjusting inertia and acceleration coefficients. The proposed PSO method will expedite convergence toward the global optimum during the iterations. To compare the performance of the proposed NTVE-PSO method with existing PSO methods, the different practical load types of Taiwan power system (Taipower) are utilized for time series prediction of one-day ahead and five-days ahead. Simulation results illustrate that the proposed NTVE-PSO-RBFNN has better forecasting accuracy and computational efficiency for different electricity demands than the other PSO-RBFNNs.