This item is printed on demand - it takes 3-4 days longer - Neuware -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 figuresxList of tablesxiIntroduction11.Design attributes in ANN31.1ANN models41.1.1Node level71.1.2Network level91.1.3Training level91.2Data and data representation101.3Global system design121.4Hardware and software implementation131.5Characteristics of ANNs151.5.1Advantages of ANNs151.5.2Limitations and concerns162.Technical process models and engineering methods182.1Why use an engineering method 182.2Evolutionary model of engineering discipline202.3Overview of technical process models222.3.1Taxonomy of technical process models242.3.2Prototyping252.3.3Incremental method262.3.4Strict contractual approach262.3.5Deciding on process models and methods262.3.6Examples of process models272.3.7Representation of process models272.4Quality criteria of process models293.Current engineering methods for ANNs303.1Why a special method for ANNs 303.1.1Are conventional engineering methodologies suitable for ANNs 303.2Methods for expert systems313.3System identication methods353.4Bailey and Thompson373.4.1Criticism433.5Medsker and Liebowitz443.6Jones and Franklin453.7Schalko473.8Karayiannis and Nicolaos483.8.1Criticism493.9Nelson and Illingworth503.9.1Criticism513.10Whittington and Spracklen523.10.1Criticism563.11Lawrence and Andriola573.11.1Criticism583.12General criticism of current methodologies584.Proposed design and development method604.1Development process614.1.1Requirement analysis654.1.2Specication684.1.3Data and domain analysis704.1.4Architectural design764.1.5Detailed ANN design844.1.6ANN implementation924.1.7Training934.1.8Monitoring training944.1.9ANN quality evaluation954.1.10ANN verication and validation954.1.11System link964.1.12System verication and validation974.1.13Delivery and production974.1.14Maintenance974.2Risk analysis984.2.1Engineers logbook1004.2.2Review of stage results1014.3Reuse1014.4Integration of ANN development1044.5Criticism104Conclusion107Appendix109Bibliography111 144 pp. Englisch.

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T�tulo: A Design and Development Method for Artificial Neural Network Projects
Editor: Diplom.De Jan 2000
Fecha de publicaci�n: 2000
Idioma: Ingl�s
ISBN 10: 3838620216
ISBN 13: 9783838620213
Encuadernaci�n: Taschenbuch
Estado: Neu

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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 3.4.1Criticism43 3.5Medsker and Liebowitz44 3.6Jones and Franklin45 3.7Schalko47 3.8Karayiannis and Nicolaos48 3.8.1Criticism49 3.9Nelson and

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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 3.4.1Criticism43 3.5Medsker and Liebowitz44 3.6Jones and Franklin45 3.7Schalko47 3.8Karayiannis and Nicolaos48 3.8.1Criticism49 3.9Nelson and

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