โ Scribed by Ross, Timothy J.
No coin nor oath required. For personal study only.
Measure what is measurable, and make measurable what is not so.
It is often difficult or impossible to accurately model complicated natural processes or engineered systems using a conventional nonlinear mathematical approach with limited prior knowledge. Ideally, the analyst uses the information and knowledge gained from prior experiments or trials with the system to develop a model and predict the outcome, but for new systems where little is known or where experimental analyses are too costly to perform, prior knowledge and information is often unavailable. This lack of data on, or extensive knowledge of, the system makes developing a model using conventional means extremely difficult and often impossible. Furthermore, forming a linguistic rule-base of the system may be impractical without conducting additional observations. Fortunately, for situations such as these, fuzzy modeling is very practical and can be used to develop a model for the system using the 'limited' available information. Batch least squares (BLS), recursive least squares (RLS), gradient method (GM), learning from example (LFE), modified learning from example (MLFE), and clustering method (CM) are some of the algorithms available for developing a fuzzy model [Passino and Yurkovich, 1998]. The choice of which method to implement depends on factors such as the amount of prior knowledge of the system to be modeled. These methods, which are referred to as automated methods, are provided as additional procedures to develop membership functions, like those in Chapter 6, and to provide rules as well.
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