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Automated Network Fault Management

dc.contributor.authorViswanathan, P.en_US
dc.date.accessioned2007-05-23T10:03:25Z
dc.date.available2007-05-23T10:03:25Z
dc.date.issued1996en_US
dc.identifier.urihttp://hdl.handle.net/1903/5836
dc.description.abstractWith the recent growth of telecommunication networks, fault management has gained much importance. Since it is difficult for humans to manage large networks, automation of many of these functions has attracted much attention. Some of the ideas proposed for automating such functions include the use of artificial intelligence techniques. Neural help to analyze large volumes of numerical data. Expert systems help to analyze observed symptoms and identify the cause using a rule-based approach. However, research in artificial intelligence has shown that when either of these two methods is used alone, several weaknesses are observed in the resulting system. Thus, some other methodology would be required for tackling such large problems.<P>In this thesis, an approach involving the use of a hybrid system involving both neural networks and expert systems for performing automated network fault management is investigated. Data networks using the X.25 protocol are considered. A minimum cost routing scheme is used for re-routing future calls given the occurrence of a fault. A method for partitioning the data (obtained from the X.25 network) between the neural network and the expert system is suggested. Radial basis function networks are used as the neural network architecture for performing fault classification using performance data. Queries are provided for the expert system to determine the type of fault that occurred using the results of the neural network, together with alarms, SNMP traps, and X.25 SNMP statistics.en_US
dc.format.extent329304 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; MS 1996-14en_US
dc.relation.ispartofseriesCSHCN; MS 1996-3en_US
dc.subjectfault diagnosisen_US
dc.subjectneural networksen_US
dc.subjectsimulationen_US
dc.subjectnetwork managementen_US
dc.subjectexpert systemsen_US
dc.subjectSystems Integration Methodologyen_US
dc.titleAutomated Network Fault Managementen_US
dc.typeThesisen_US
dc.contributor.departmentISRen_US
dc.contributor.departmentCSHCNen_US


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