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**Topic under **Control Mode

**Source:** dept.lamar.edu

**File size:** **968.52 KB**

**File type:** **pdf**

**Last download on:**
Thu Apr 12, 2018 01:56:32 AM

__Short Desciption__:

Fuzzy-logic-based process modeling using limited experimental data H.H. Lou, Y.L. Huang* Department of Chemical Engineering and Materials Science, Wayne State University, 5050 Anthony ...

__Summary__:

Process modeling with limited experimental data is always a dicult task. It becomes even more dicult if the process is
highly nonlinear and is characterized by multiple inputs and outputs. Under these circumstances, fuzzy logic may show its
capabilities for model development. In this paper, an ecient fuzzy modeling methodology is introduced. The resulting fuzzy
model consists of a number of fuzzy implications, each of which is of an IF±THEN form. The IF part consists of a set of
logically related antecedents, while the THEN part contains a consequent expressed as a set of linear models. To ensure model
simplicity and to accelerate the modeling process, an effective model-development route has been developed. To guarantee the
model's reliability, a t-test-based non-linearity analysis is proposed when each fuzzy implication is developed. The efficacy of the
methodology is demonstrated by modeling two nonlinear industrial processes. # 2000 Elsevier Science Ltd. All rights reserved.

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