Training Spiking Neural Models Using Artificial Bee Colony
نویسندگان
چکیده
منابع مشابه
Training Spiking Neural Models Using Artificial Bee Colony
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, severa...
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The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison wit...
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The Artificial Bee Colony Algorithm (ABC) is a heuristic optimization method based on the foraging behavior of honey bees. It has been confirmed that this algorithm has good ability to search for the global optimum, but it suffers from the fact that the global best solution is not directly used, but the ABC stores it at each iteration, unlike the particle swarm optimization (PSO) that can direc...
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ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2015
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2015/947098