Files in this item
Files | Description | Format |
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application/pdf ![]() | Structural Research Series 590 |
Description
Title: | Data-Based Mathematical Modeling: Development and Application, or How to Build a Mapping Neural Network |
Author(s): | Banan, Mahmoud-Reza; Hjelmstad, K.D. |
Subject(s): | Neural networks
Fuzzy subsets Mathematical modeling |
Abstract: | This report presents a general method for developing data-based mathematical models for complex problems with large data bases. The method uses the Monte Carlo method in conjunction with a hierarchical adaptive random partitioning scheme with fuzzy sub domains (Me-HARP). Me-HARP provides an environment for simultaneously building and training a mapping neural network. The method is self-organizing and can operate with minimal external adjustment. It can interactively accept knowledge and provide guidance for efficiently improving the model and the data base. The Me-HARP environment enjoys a large-scale granularity produced by the Monte Carlo parallelism and the geometric parallelism achieved by partitioning the input space. We study the performance of the Me-HARP method by applying it to an experimental data base on pavement performance under a variety of environmental and traffic conditions. Numerical simulations are used throughout the report to demonstrate that the method is able to deal with high-dimensional, noisy, non-homogeneous data. The Me-HARP method leads to a novel model selection criterion and an original framework for classifying data-fitting problems, and can be used to answer fundamental questions in data-based mathematical modeling. These questions include: What is the confidence level in the constructed model and the data base? What is the optimal functional structure of the model for noisy data? How appropriate is a particular parametric model for the given data? The Me-HARP method established an environment for unifying existing mathematical modeling techniques in statistics, approximation theory, information theory, system identification, and neural networks. |
Issue Date: | 1994-04 |
Publisher: | University of Illinois Engineering Experiment Station. College of Engineering. University of Illinois at Urbana-Champaign. |
Series/Report: | Civil Engineering Studies SRS-590 |
Genre: | Technical Report |
Type: | Text |
Language: | English |
URI: | http://hdl.handle.net/2142/14211 |
Sponsor: | National Science Foundation Grant CES 86-58019 Army Research Office Contracts DAAL-03-87-K-006, DAAL-03-86-G-0186, and DAAL-03-86-0188 |
Date Available in IDEALS: | 2009-11-12 |