TAILIEUCHUNG - Fuzzy Systems Part 6

Tham khảo tài liệu 'fuzzy systems part 6', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Adaptive Neuro-Fuzzy Systems 91 2 wf _ O5 overalloutput 2 wf 2 5 i 4. Modeling with neuro-fuzzy systems Whatever may be the adopted vision of fuzzy model t o different phases must be carried out in fuzzy modeling designated as structural and parametric identification. Structural identification consists of determining the structure of the rules . the number of rules and the number of fuzzy sets used to partition each variable in the input and output space so as to derive linguistic labels. Once a satisfactory structure is available the parametric identification must follow for the fine adjustment of the position of all membership functions together with their shape as the main concern. As seen before to overcome the limitations of using expert knowledge in defining the fuzzy rules data driven methods to create fuzzy systems are needed. With such methods both structure and parameters are derived from scratch relying only on the training data. There are several ways that structure learning and parameter learning can be combined in a neuro-fuzzy system. They can be performed sequentially structure learning is used first to find an appropriate structure of the fuzzy rule base and then parameter learning is used to identify the parameters of each rule. In some neuro-fuzzy systems the structure is fixed and only parameter learning is performed. Algorithms inspired by neural network learning often do parameter learning. Structure learning on the other hand is usually not from neural networks. Indeed many different approaches exist to automatically determine the structure of neural networks but none of them is appropriate to perform structure identification in neuro-fuzzy models. In the following different methods are presented that used for structure and parameter identification in neuro-fuzzy systems. There may be a lot of structure parameter combinations which make the fuzzy model to behave satisfactorily hence the search for the best model is not an easy task. As a .

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