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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Modular Neural Networks
verfasst von : Anupam Shukla, Ritu Tiwari, Rahul Kala
Erschienen in: Towards Hybrid and Adaptive Computing
Verlag: Springer Berlin Heidelberg
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Modular Neural Networks are use of a number of Neural Networks for problem solving. Here the various neural networks behave as modules to solve a part of the problem. The entire task of division of problem into the various modules as well as the integration of the responses of the modules to generate the final output of the system is done by an integrator. In this chapter we first look at the various Modular Neural Network models. Here we would mainly study two major models. The first model would cluster the entire input space with each module responsible for some part of it. The other model would make different neural networks work over the same problem. Here we would be using a response integration technique for figuring out the final output of the system. The other part of the chapter would present Evolutionary Modular Neural Networks. We would first present a simple genetic approach and then a co-evolutionary approach for this evolution of the entire Modular Neural Network.