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Modular construction of fuzzy logic ...

Lin, Yuetong.

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Modular construction of fuzzy logic control systems using wavelet networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modular construction of fuzzy logic control systems using wavelet networks./
作者:	Lin, Yuetong.
面頁冊數:	154 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-03, Section: B, page: 1697.
Contained By:	Dissertation Abstracts International66-03B.
標題:	Engineering, System Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3167254
ISBN:	0542025752

Modular construction of fuzzy logic control systems using wavelet networks.
Lin, Yuetong.

Modular construction of fuzzy logic control systems using wavelet networks. - 154 p.

Source: Dissertation Abstracts International, Volume: 66-03, Section: B, page: 1697.

Thesis (Ph.D.)--The University of Arizona, 2005.

This dissertation presents a novel approach to combining wavelet networks and multi-layer feedforward network for fuzzy logic control systems. Most of the existing methods focus on implementing the Takagi-Sugano fuzzy reasoning model and have demonstrated its effectiveness. However, these methods fail to keep the knowledge structure, which is critical in interpreting the learning process and providing insights to the working mechanism of the underlying systems. It is our intention here to continue the previous research by the PARCS group in this area by utilizing individual subnets to implement decision-making process with the fuzzy logic control systems based on the Mamdani model. Center Average defuzzification has seen its implementation by a neural network so that a succinct network structure is obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time. Offline orthogonal least squares method is used for training the wavelet subnets and the overall systems is updated using the steepest descent algorithm. Simulation results have shown the efficacy of this new approach in applications including system modeling and time series prediction.

ISBN: 0542025752Subjects--Topical Terms:

1018128
Engineering, System Science.

Modular construction of fuzzy logic control systems using wavelet networks.
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502 $a Thesis (Ph.D.)--The University of Arizona, 2005.
520 $a This dissertation presents a novel approach to combining wavelet networks and multi-layer feedforward network for fuzzy logic control systems. Most of the existing methods focus on implementing the Takagi-Sugano fuzzy reasoning model and have demonstrated its effectiveness. However, these methods fail to keep the knowledge structure, which is critical in interpreting the learning process and providing insights to the working mechanism of the underlying systems. It is our intention here to continue the previous research by the PARCS group in this area by utilizing individual subnets to implement decision-making process with the fuzzy logic control systems based on the Mamdani model. Center Average defuzzification has seen its implementation by a neural network so that a succinct network structure is obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time. Offline orthogonal least squares method is used for training the wavelet subnets and the overall systems is updated using the steepest descent algorithm. Simulation results have shown the efficacy of this new approach in applications including system modeling and time series prediction.
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