Master's Thesis from the year 1995 in the subject Engineering -
Artificial Intelligence, University of Massachusetts - Dartmouth
(Unbekannt), language: English, abstract: Inhaltsangabe: Abstract:
In the 1980s research efforts and successes made artificial neural
networks popular. Since the 1990s engineers have been using this
foundation for problem solving. But artifiial neural network
solutions for "real-world" problems are sometimes hard to find
because of the complexity of the domain and because of the vast
number of design attributes the engineer has to deal with. This
thesis provides a structured overview of attributes in the design
process of artificial neural networks and reviews technical process
models. Current development methods for artificial neural networks
are then reviewed and critiqued. The thesis concludes with a new
design and development method for artificial neural networks.
Inhaltsverzeichnis: Table of Contents: List of figuresx List of
tablesxi Introduction1 1.Design attributes in ANN3 1.1ANN models4
1.1.1Node level7 1.1.2Network level9 1.1.3Training level9 1.2Data
and data representation10 1.3Global system design12 1.4Hardware and
software implementation13 1.5Characteristics of ANNs15
1.5.1Advantages of ANNs15 1.5.2Limitations and concerns16
2.Technical process models and engineering methods18 2.1Why use an
engineering method?18 2.2Evolutionary model of engineering
discipline20 2.3Overview of technical process models22
2.3.1Taxonomy of technical process models24 2.3.2Prototyping25
2.3.3Incremental method26 2.3.4Strict contractual approach26
2.3.5Deciding on process models and methods26 2.3.6Examples of
process models27 2.3.7Representation of process models27 2.4Quality
criteria of process models29 3.Current engineering methods for
ANNs30 3.1Why a special method for ANNs?30 3.1.1Are conventional
engineering methodologies suitable for ANNs?30 3.2Methods for
expert systems31 3.3System identication methods35 3.4Bailey and
Thompson37 3.4.1Criticism
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