Modeling and characterization of plasma processes using modular neural network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Study of application model on BP neural network optimized by fuzzy clustering
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Dependable Systems
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Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of “toy” alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation