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Neural network and fuzzy logic based...

Ullah, Muhammed Zafar.

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Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation./
Author:	Ullah, Muhammed Zafar.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2000,
Description:	116 p.
Notes:	Source: Dissertation Abstracts International, Volume: 61-04, Section: B, page: 2125.
Contained By:	Dissertation Abstracts International61-04B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9969019
ISBN:	9780599738249

Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation.
Ullah, Muhammed Zafar.

Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation. - Ann Arbor : ProQuest Dissertations & Theses, 2000 - 116 p.

Source: Dissertation Abstracts International, Volume: 61-04, Section: B, page: 2125.

Thesis (Ph.D.)--Texas A&M University, 2000.

Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems.

ISBN: 9780599738249Subjects--Topical Terms:

649834
Electrical engineering.

Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation.
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245 1 0 $a Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation.
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300 $a 116 p.
500 $a Source: Dissertation Abstracts International, Volume: 61-04, Section: B, page: 2125.
500 $a Chair: S. P. Bhattacharyya.
502 $a Thesis (Ph.D.)--Texas A&M University, 2000.
520 $a Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems.
520 $a One of the major problems in portable or electric vehicle world is secondary cell charging, which shows non-linear characteristics. Portable-electronic equipment, such as notebook computers, cordless and cellular telephones and cordless-electric lawn tools use batteries in increasing numbers. These consumers demand fast charging times, increased battery lifetime and fuel gauge capabilities. All of these demands require that the state-of-charge within a battery be known. Charging secondary cells Fast is a problem, which is difficult to solve using conventional techniques. Charge control is important in fast charging, preventing overcharging and improving battery life. This research work provides a quick and reliable approach to charger design using Neural-Fuzzy technology, which learns the exact battery charging characteristics. Neural-Fuzzy technology is an intelligent combination of neural net with fuzzy logic that learns system behavior by using system input-output data rather than mathematical modeling.
520 $a The primary objective of this research is to improve the secondary cell charging algorithm and to have faster charging time based on neural network and fuzzy logic technique. Also a new architecture of a controller will be developed for implementing the charging algorithm for the secondary battery.
590 $a School code: 0803.
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