Premium
Developing Practical Neural Network Applications Using Back‐Propagation
Author(s) -
Hegazy T.,
Fazio P.,
Moselhi O.
Publication year - 1994
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.1994.tb00369.x
Subject(s) - heuristics , computer science , artificial neural network , generalization , backpropagation , artificial intelligence , process (computing) , mathematical proof , structuring , nervous system network models , representation (politics) , machine learning , theoretical computer science , time delay neural network , types of artificial neural networks , mathematics , mathematical analysis , geometry , finance , politics , political science , law , economics , operating system
In the past few years, neural networks have emerged as a problem‐solving technique with capabilities suited to many civil engineering problems. Among the various neural network paradigms available, back‐propagation is by far the most utilized for its relatively simple mathematical proofs and good generalization capabilities. Despite its capabilities, back‐propagation suffers from several problems that hinder the development of practical neural network applications. These include slow training, ill‐defined knowledge representation and problem structuring, and nonguided design of an optimal network configuration for adequate generalization. This paper represents an effort to guide the process of developing practical neural network applications using back‐propagation. The paper starts with a brief description of back‐propagation mathematics. Some of the heuristics and techniques used to overcome back‐propagation problems, particularly lack of generalization, are identified and outlined, along with areas of potential improvements to the paradigm. An application development methodology is proposed utilizing the identified heuristics and techniques. The methodology provides a structured framework for designing and implementing practical neural network applications with less effort.