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W.C. Carpenter* and J-F. Barthelemy+

*Department of Civil Engineering and Mechanics, University of South Florida, Tampa, Florida, United States of America
+NASA Langley Research Center, Hampton, Virginia, United States of America

doi:10.4203/ccp.16.2.1

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W.C. Carpenter, J-F. Barthelemy, "Common Misconceptions About Neural Networks as Approximators", in B.H.V. Topping, A.I. Khan, (Editors), "Neural Networks & Combinatorial Optimization in Civil & Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 11-18, 1993. doi:10.4203/ccp.16.2.1

Abstract

A current trend in scientific and engineering computing is to use neural network approximations instead of polynomial approximations or other types of approximations involving mathematical functions. A number of misconceptions have arisen concerning neural networks as approximations. The goal of this paper is to eliminate these misconceptions. In so doing, the paper examines the computational efficiency of neural network approximations compared to polynomial approximations, examines the effect of using under-determined neural network approximations, examines the effect of design selection on the quality of neural network approximations, examines the effect of over-training neural networks, and examines the computing time required to train neural networks compared to the time to develop polynomial approximations.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 16 NEURAL NETWORKS & COMBINATORIAL OPTIMIZATION IN CIVIL & STRUCTURAL ENGINEERING Edited by: B.H.V. Topping and A.I. Khan Paper II.1 Common Misconceptions About Neural Networks as Approximators W.C. Carpenter* and J-F. Barthelemy+ *Department of Civil Engineering and Mechanics, University of South Florida, Tampa, Florida, United States of America +NASA Langley Research Center, Hampton, Virginia, United States of America doi:10.4203/ccp.16.2.1 purchase the full-text of this paper Full Bibliographic Reference for this paper W.C. Carpenter, J-F. Barthelemy, "Common Misconceptions About Neural Networks as Approximators", in B.H.V. Topping, A.I. Khan, (Editors), "Neural Networks & Combinatorial Optimization in Civil & Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 11-18, 1993. doi:10.4203/ccp.16.2.1 Abstract A current trend in scientific and engineering computing is to use neural network approximations instead of polynomial approximations or other types of approximations involving mathematical functions. A number of misconceptions have arisen concerning neural networks as approximations. The goal of this paper is to eliminate these misconceptions. In so doing, the paper examines the computational efficiency of neural network approximations compared to polynomial approximations, examines the effect of using under-determined neural network approximations, examines the effect of design selection on the quality of neural network approximations, examines the effect of over-training neural networks, and examines the computing time required to train neural networks compared to the time to develop polynomial approximations. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £34 +P&P)
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