A neuron-output-significant-index-based self-organization pruning algorithm for S-LINN
| Autor: | Hui Yang, Lizhen Dai, Gang Yang, Lu Rongxiu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Structure (mathematical logic)
Self-organization Artificial neural network Computer science 020208 electrical & electronic engineering Process (computing) 02 engineering and technology computer.software_genre Nonlinear system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pruning (decision trees) Data mining computer |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2017.7966189 |
| Popis: | For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, a self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm is to build a modified significant index function to evaluate the significance of hidden neurons. A preprocessing training of the initial network with assumed redundant hidden neurons will be allowed in the tuning process. A subsequent learning after the self-organization pruning process is also implemented to optimize the parameters of pruned network. The proposed self-organizing approach has been tested on two benchmark problems in the areas of breast cancer dataset diagnosis and nonlinear dynamic system identification problem. Simulation results demonstrate that the proposed method has good exploration and exploitation capabilities in terms of searching the optimal structure and parameters for S-LINN. The proposed method also can be used to optimal standard neural network. |
| Databáze: | OpenAIRE |
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