Competitive learning algorithms for vector quantization
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Self-organizing maps
Handbook of Neural Computation
Handbook of Neural Computation
Avoiding Pitfalls in Neural Network Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Winner-take-all neural networks using the highest threshold
IEEE Transactions on Neural Networks
Cooperative information maximization with Gaussian activation functions for self-organizing maps
IEEE Transactions on Neural Networks
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This correspondence describes our attempts of incorporating particle swarm features into competitive learning. We first outline our reinterpretation of the symbols and notations used in particle swarm optimisation (PSO) algorithms. Three versions of modifications to the classical frequencysensitive competitive learning are presented. A new contraction/expansion phenomenon is illustrated. We then examine the effect of introducing particle swarm like features in our lotto-type competitive learning. Experimental results indicate that, like the PSO algorithms, a careful selection of the values for the control parameters is necessary for the successful convergence of particles. With the new modifications, we show experimentally that the modified algorithm can behave both similar to PSO algorithms and the original lotto-type competitive learning algorithms.