A global learing algorithm for a RBF network
Neural Networks
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
A dynamic K-winners-take-all neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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A new parallel neural network clustering algorithm based particle swarm optimisation is presented. A large number of pieces of evidence are clustered into subsets. A nonlinear connection function is adopted in this neural network clustering algorithm, the centre of connection function is used to a particle, the whole neural network clustering object function can be expressed. A numerical example has been used to illustrate the effect of the algorithm on the characteristics clustering of electric loads. Many sets of load data measured from a power system have been dealt with using the method. The results of the study clearly indicate that the proposed method is very useful to load characteristics clustering for power system.