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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jia, T.J. |
| Copyright Year | 2008 |
| Description | Author affiliation: Coll. of Electron. & Inf., Shanghai Dianji Univ., Shanghai (Jia, T.J.) |
| Abstract | A typical characteristic of Bayesian network topology is dependences of each variable within the network, which makes it impossible to optimize variables. This problem is solved by the developed approach to Bayesian network construction based on self-organizing genetic algorithm (SGA) from knowledge base. The genetic algorithm (GA) is improved by self-organizing organism and an effective operator is provided to search the global optimum value in order to avoid an early convergence for a normal GA algorithm. At last the experiment results and the convergence of SGA are discussed. |
| Starting Page | 37 |
| Ending Page | 40 |
| File Size | 248727 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769532585 |
| DOI | 10.1109/ISECS.2008.23 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-08-03 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Gallium Bayesian learning based on Knowledge base Network topology Self-organizing Genetic Algorithm (SGA) Bayesian methods Knowledge based systems network safety applies Topology Bayesian network Convergence Construction industry |
| Content Type | Text |
| Resource Type | Article |
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