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LEARNING PROCEDURES FOR NEURAL NETWORKS BASED ON EVOLUTIONARY ALGORITHMS
LEARNING PROCEDURES FOR NEURAL NETWORKS BASED ON EVOLUTIONARY ALGORITHMS
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机译:基于进化算法的神经网络学习程序
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摘要
The invention relates to a method for determining weights of a function which maps at least one input value to at least one output value, in such a way that an error is minimized which indicates a deviation of the at least one output value from a target state, the totality of the weights, which forms the function, represents an individual. Starting from a first population, which comprises several individuals, a best individual is determined using a genetic / evolutionary algorithm, with all other individuals in the population being discarded. By varying the individual weights of the best individual, additional individuals are generated which, together with the best individual, form a new population. The new population runs through the genetic / evolutionary algorithm again until at least one predetermined termination condition is met. The variation of the individual weights for determining the individual individuals of the new population takes place on the basis of a predetermined percentage of the individual weights of the best individual. A search for a global extreme of a function can be facilitated by the disclosed method.
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