Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 20, Issue 1, Pages 63-74Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065710002243
Keywords
Repair algorithm; sensitivity analysis (SA); RBF neural network; applications
Categories
Funding
- National 863 Scheme Foundation of China [2007AA04Z160]
- National Science Foundation of China [60674066, 60873043]
- Ministry of Chinese Education [200800050004]
- Beijing Municipal Natural Science Foundation [4092010]
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This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.
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