| 書目名稱 | Fuzzy Model Identification for Control | | 編輯 | János Abonyi | | 視頻video | http://file.papertrans.cn/352/351305/351305.mp4 | | 圖書封面 |  | | 描述 | Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima- tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in- formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap- proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec- tive us | | 出版日期 | Book 2003 | | 關(guān)鍵詞 | Control Algorithms; Matlab; control; control theory; dynamical systems; fuzzy control; intelligent control | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4612-0027-7 | | isbn_softcover | 978-1-4612-6579-5 | | isbn_ebook | 978-1-4612-0027-7 | | copyright | Birkh?user Boston 2003 |
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