| In this thesis a new approach to fuzzy system identification is proposed. The proposed approach consists of two phases. The first phase involves a baseline design to effectively identify a prototype fuzzy system for a target system from a collection of input-output data pairs. This is implemented by incorporating the subtractive clustering method to determine the number of clusters and the fuzzy c-means (FCM) clustering algorithm to build the actual clusters. The second phase involves a fine-tuning process for the parameters of the prototype fuzzy system identified in the baseline design. This process can be realized by using the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying to both the function approximation and classification types of problems which are the truck backer-upper and Iris flower classification problems, respectively. A comprehensive performance analysis which study the learning behavior of the proposed approach for both the problems is then conducted for further confirmation. Finally, the BOFCM-FCA-based approach is introduced to enhance the effectiveness of the proposed approach. |
