A constrained orthogonal least-squares method for generating TSK fuzzy models: Application to short-term load forecasting
✍ Scribed by Paris A. Mastorocostas; John B. Theocharis; Vassilios S. Petridis
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 237 KB
- Volume
- 118
- Category
- Article
- ISSN
- 0165-0114
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✦ Synopsis
In this paper, an orthogonal least-squares (OLS) based modeling method is developed, named the constrained OLS (C-OLS), for generating simple and e cient TSK fuzzy models. The method is a two-stage model building technique, where both premise and consequent identiÿcation are simultaneously performed. The fuzzy system is considered as a linear regression model by decomposing the TSK model into a collection of generic rules. The C-OLS algorithm is employed at stage-1 to identify the structure of the model. Given a model building data set, the algorithm selects a subset of most signiÿcant regressors which should be included in the model. Based on the similarity measure, a classiÿcation tool is developed, which organizes the selected terms into groups with similar premise parts, forming TSK rules. Additionally, input variable selection for the consequent part is performed. The resulting model is reduced in complexity by discarding the unnecessary terms, and is optimized at stage-2 using a richer training data set. This method is used to generate fuzzy models for a real-world problem, the load forecasting of the Greek power system. Extensive simulation results are given and discussed, demonstrating the e ectiveness of the suggested method.