Abstract
Back-propagation neural network model (BPNN) is an intelligent computational model based on stylebook learning. This model is different from traditional adaptability symbolic logic reasoning method based on knowledge and rules. At the same time, BPNN model has shortcoming such as: slowly convergence speed and partial minimum. During the process of adaptability evaluation, the factors were diverse, complicated and uncertain, so an effectual model should adopt the technique of data mining method and fuzzy logical technology. In this paper, the author ameliorated the backpropagation of BPNN and applied fuzzy logical theory for dynamic inference of fuzzy rules. Authors also give detail description on training and experiment process of the novel model.
Original language | English |
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Article number | 71461B |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 7146 |
DOIs | |
State | Published - 2008 |
Externally published | Yes |
Event | Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses - Guangzhou, China Duration: 28 Jun 2008 → 29 Jun 2008 |
Keywords
- Adaptability evaluation
- Back propagation
- Intelligent compute
- Spatial data mining