This thesis presents an aggregate system analysis with fuzzy methodology for interpretation, diagnosis and prediction of the behavior of the complex systems.; The proposed systematic fuzzy modeling has three significant characteristics: (a) an improved fuzzy clustering approach with covariance-norm matrix, (b) an improved strategy for input variable selection and assignment of input-output membership functions, and (c) an appropriate parametrized reasoning mechanism.; Initially, we surveyed the literature on fuzzy system modeling and discussed different approaches to fuzzy cluster analysis. Some of these procedures revealed shortcomings in with real-world data.; Having developed the proposed model and its related algorithms, we tested it on four sets of data from real-world case studies. We found our approach better suited the real-world problems, including the interactions and correlations among complex sets of data and variables. It also presented a suitable strategy for determining the number of clusters and the degree of fuzziness of the system.; We then introduced the index and methodology for significant input selection and assignment of input membership functions and considered possible correlations between input variables, using a Mahalanobis distance measure. The parametrized inference mechanism determined the actual parameters of the system based on the data. We tuned the input-output membership functions through a supervised-learning procedure to reduce the system's error.; The proposed fuzzy methodology then was applied for system analysis, diagnosis and prediction of three complex problems in continuous casting: tardiness, mixed-zone effects, and total costs of tardiness and mixed zones. In each case, we compared the results with those of previous fuzzy models with identity-norm matrices and Euclidean distance measures and with a classical multiple-regression model. The results show that the proposed fuzzy methodology is superior with respect to identifying the critical rules, critical variables, and error minimization.
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