類神經網路的線性最佳化

機構典藏 > College of Engineering > Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering > Proceeding > Item 987654321/38015

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Title:	類神經網路的線性最佳化
Other Titles:	Linear Engineering Optimization Using Artificial Neural Network
Authors:	楊宗龍;史建中
Contributors:	淡江大學機械與機電工程學系
Keywords:	類神經網路;倒傳遞網路;線性最佳化;設計空間;預應力混凝土梁;目標函數;近似值訓練;Artificial Neural Network;Back-Propagation Network;Linear Optimization;Design Space;Prestressed Concrete Beam;Objective Function;Approximate Training
Date:	1993-12
Issue Date:	2010-01-11 14:31:34 (UTC+8)
Publisher:	交通大學
Abstract:	本文應用倒傳遞網路模式以發展類神經網路的線性最佳化設計,其主要步驟為(1)數學公式化(2)網路訓練及測試(3)縮小全域搜尋及最佳化,收斂到最佳值為止。本文所提出的方法,可免除傳統simplex法當設計變數及拘束條件大量增加時的繁複數值計算,其亦和simplex法相同的得到全域最佳解。網路的繼續發展及改良,將有助於提高最佳值的精確度。文中以五個線性雙變數函數的最佳化設計問題為例進行模擬與分析,以驗證所提的類神經網路線性最佳化設計方法。 The paper presents an approach of Artificial Neural Network (ANN) in Linear Programming (LP) for engineering design. Back Propagation Network (BPN) is applied to this approach with 2-0-1 structure. One can divide this method into three primary steps, that are mathematical formulation, training & testing of network, and global optimum search. A technique of reducing searching area has been introduced for accelerating convergence. This approach can straightly produce a considerable satisfying solution to avoid the large numerical computation and analysis in conventional Simplex Method. This developed algorithm is based on the N- dimensional design space. For the clear expression, several 2-D design examples are presented to demonstrate the method and the feasibility of the proposed approach. It is noted that the method can be applicable to higher dimensional design problems.
Relation:	中國機械工程學會第十屆學術研討會控制組論文集=Proceedings of the Tenth National Conference of the Chinese Society of=Mechanical Engineers，頁141-147
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Proceeding

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類神經網路的線性最佳化+_中文摘要.pdf		136Kb	Adobe PDF	144	View/Open
類神經網路的線性最佳化+_英文摘要.pdf		98Kb	Adobe PDF	82	View/Open

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