| Autor: |
Oh Sung-Kwun, Kim Wan-Su, Lee In-Tae, Kim Hyun-Ki |
| Rok vydání: |
2005 |
| Předmět: |
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| Zdroj: |
Journal of Fuzzy Logic and Intelligent Systems. 15:846-851 |
| ISSN: |
1598-7078 |
| DOI: |
10.5391/jkiis.2005.15.7.846 |
| Popis: |
In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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