Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques
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This paper develops a new approach to building Sugeno-type models. The essential idea is to separate premise identification from consequence identification, while these are mutually related in the previous methods. A fuzzy discretization technique is suggested to determine the premise of the model, and an orthogonal estimator is provided to identify the consequence of the model. The orthogonal estimator can provide information about the model structure, or which terms to include in the model, and final parameter estimates in a very simple and efficient manner. The utility of the proposed approach is illustrated using the well-known gas furnace data of Box and Jenkins.
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