empirical_models

empirical_models - Empirical Modeling 1. 2. 3. 4. 5. 6....

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Empirical Modeling 1. Introduction 2. Regression models 3. First-order transfer function models 4. Second-order transfer function models 5. Integrating models 6. Matlab System Identification Toolbox
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Motivation Fundamental models » Derived from conservation principles » Typically comprised of ODEs » Preferred modeling approach when possible Limitations of fundamental modeling » Often lack fundamental knowledge of process » Unknown parameters must be specified » Complex models may not be suitable for controller design Alternative approach » Derive model directly from process data » Procedure known as process identification » Yields empirical models
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Process Identification Basic idea » Vary process input u ( t ) » Collect measurements of the process output y ( t ) » Use data to construct dynamic model M relating u ( t ) and y ( t ) » Goal is to obtain the simplest model possible Limitations » Model only represents process dynamics over range of data collected » No fundamental knowledge is gained
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General Modeling Procedure 1. Formulate model objectives
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This document was uploaded on 02/04/2012.

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empirical_models - Empirical Modeling 1. 2. 3. 4. 5. 6....

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