Tuning Neural Networks by Both Connectivity and Size
Information Technology: New Generations（2010）
摘要
This paper presents a new tuning algorithm for a neural network which not only considers the weighting but also the size and connectivity of the network. The approach is done by two parts: the addition of the switch-based hidden nodes and the application of a modified fitness function. The new model is tested by using two simple logic functions. The results show that the modifications lead to the creation of simpler networks, without sacrificing any accuracy or training time in the process. In addition, the lessened human interaction aspect of the new algorithm is also significant in real-time applications.
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关键词
modified fitness function,switch-based hidden node,tuning neural networks,neural network,lessened human interaction aspect,simpler network,new model,simple logic function,new algorithm,real-time application,new tuning algorithm,network topology,fitness function,artificial neural networks,human interaction,accuracy,tuning,switches,neural nets,neural networks,genetic algorithm,genetic algorithms,hidden node,computer networks,information technology
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