Data-based mathematical modeling: Development and application

This research study presents the mathematical basis for building the MC-HARP data-processing environment. The MC-HARP strategy determines the functional structure and parameters of a mathematical model simultaneously. A Monte Carlo (MC) strategy combined with the concept of Hierarchical Adaptive Random Partitioning (HARP) and fuzzy subdomains determines the multivariate parallel distributed mappings. The constructed mapping can be modeled as a neural network. The HARP algorithm is based on a divide-and-conquer strategy that partitions the input space into measurable connected subdomains and builds a local approximation for the mapping task. Fuzziness promotes continuity of the mapping constructed by HARP and smooths the mismatching of the local approximations in the neighboring subdomains. The Monte Carlo superposition of a sample of random partitions, reduces the localized disturbances among the fuzzy subdomains, controls the global smoothness of the mean average mapping, and improves the generalization of the constructed mapping.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Banan, Mahmoud-Reza

Contributors dc:contributor

• Hjelmstad, Keith D.

• Statistics
• Engineering, Civil
• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1995 Banan, Mohmoud-Reza

Language dc:language

eng

Identifier

AAI9522079
(UMI)AAI9522079

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/22097