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Particle swarm optimization (PSO) algorithm is a random search method based on population evolution. At present, most of the convergence and search stability of the study are using a method of analyzing deterministic systems. Although these studies have achieved fruitful results, but they ignore the randomness of the PSO algorithm which is the most important feature. The complexity and uncertainty...
Artificial Colony Algorithm (ABC) is a random optimization algorithm based on swarm search behavior, which is widely used in recent years. Considering the slow convergence and ease of falling into the local optimum of basic ABC, researchers try various modified methods to overcome its shortcomings. Different modified ABC algorithms have different characteristics and application scope. In order to...
The feedforward neural networks trained with the online backpropagation (BP) learning algorithm have been widely studied in various areas of scientific research and engineering applications. In this paper we further study the convergence property of the online BP learning algorithm. Unlike the existing convergence analysis mainly focusing on the convergence of the gradient sequence of the error functions,...
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