NDLI: Improved neural network modeling approach for engineering applications

Content Provider	IEEE Xplore Digital Library
Author	Ming Lu
Copyright Year	2002
Description	Author affiliation: Dept. of Civil & Struct. Eng., Hong Kong Polytech. Univ., Kowloon, China (Ming Lu)
Abstract	The multilayer perceptron (MLP) neural networks (NN) trained with back propagation algorithm (or BPNN) is the most popular and widely used network paradigm in engineering applications. Two setbacks of NN modeling are (1) very slow rate of learning and (2) trial-and-error problem-dependent selection of architectural/learning parameters. This paper focuses more on the trial and error methodologies used in NN engineering applications. An improved trial and error modeling approach is proposed to synchronize the optimization of BPNN's architecture and learning performance. In addition, the new approach addresses several other common concerns on the use of the BPNN technique in engineering applications, such as (1) detecting and avoiding over-fitted models; (2) decoding the state of intelligence of "black box" NN models; and (3) augmenting the ability of NN to extrapolate beyond the training data. A case study of applying BPNN to characterize the geographical terrain slope is presented to illustrate the advantages of the new modeling approach. A total of 66 patterns were captured from a terrain geographical information system (GIS) recently developed for investigating six test areas in the Losses Plateau, Northwestern China. A computer program was coded in house to implement the proposed approach and arrive at the final model for characterizing the mean slope. The model is able to extrapolate beyond the training data to a certain extent and demonstrates "intelligent" reasoning capability in relating inputs to the output.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1810
Ending Page	1814
File Size	369564
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1198986
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Neural networks Training data Backpropagation algorithms Iterative algorithms Structural engineering Data engineering Decoding Information systems Geographic Information Systems System testing
Content Type	Text
Resource Type	Article

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