Intelligent Neuro-Fuzzy Computing with Complex Fuzzy Sets and ARIMA Models
| Autor: | Tai-Wei Chiang, 江泰緯 |
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| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 Ever since the initiate of the theory of complex fuzzy sets (CFSs), a new vision has dawned upon fuzzy systems and their variants. Although there has been considerable development made in determining the properties of CFSs, the research on complex fuzzy system designs and applications of this concept is found rarely. In this dissertation, we present a novel self-organizing complex neuro-fuzzy intelligent approach using CFSs for the applications of system modeling. The proposed approach integrates a complex neuro-fuzzy system (CNFS) using CFSs and auto-regressive integrated moving average (ARIMA) models to form the proposed computing model, called the CNFS-ARIMA. A class of Gaussian complex fuzzy sets is proposed to describe the premise parts of fuzzy If-Then rules, whose consequent parts are specified by ARIMA models. A CFS is an advanced fuzzy set whose membership degrees are complex-valued within the unit disc of the complex plane, expanding the capability of membership description. With the nature of CFS, the proposed CNFS models have excellent nonlinear mapping capability. Moreover, the output of CNFS-ARIMA is complex-valued, of which the real and imaginary parts can be used for two different functional mappings, respectively. This is the so-called dual-output property. For the formation of CNFS-ARIMA, structure learning and parameter learning are involved to self-organize and self-tune the proposed model. For the structure learning phase, a FCM-based splitting algorithm (FBSA) is used to automatically determine the initial knowledge base of the CNFS-ARIMA. The PSO-RLSE hybrid learning algorithm is proposed for the purpose of fast learning, integrating the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE). A number examples of time series are used to test the proposed approach, whose results are compared with those by other approaches. Moreover, real-world applications of system modeling including function approximation and time series are used for the proposed approach to perform the dual-output forecasting experiments. The experimental results indicate that the proposed approach shows excellent performance. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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