Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control
Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, tw...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/12157/1/13.pdf http://repo.uum.edu.my/12157/ http://www.ajouronline.com/index.php?journal=AJAS&page=index |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, two attractive tools have emerged recently.These two attractive tools are: the artificial neural networks and the fuzzy logic system. One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes.To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here.Air-
fuel ratio represents complex, nonlinear and stochastic behavior.To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air-
fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time.This paper describes a fuzzy clustering method to initialize the ANFIS. |
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