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MA-Thesis / Master, die am 01.05.1995 erfolgreich an einer Universität in USA eingereicht wurde. 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. Table of Contents: |List of figures|x |List of tables|xi |Introduction|1 1.|Design attributes in ANN|3 1.1|ANN models|4 1.1.1|Node level|7 1.1.2|Network level|9 1.1.3|Training level|9 1.2|Data and data representation|10 1.3|Global system design|12 1.4|Hardware and software implementation|13 1.5|Characteristics of ANNs|15 1.5.1|Advantages of ANNs|15 1.5.2|Limitations and concerns|16 2.|Technical process models and engineering methods|18 2.1|Why use an engineering method?|18 2.2|Evolutionary model of engineering discipline|20 2.3|Overview of technical process models|22 2.3.1|Taxonomy of technical process models|24 2.3.2|Prototyping|25 2.3.3|Incremental method|26 2.3.4|Strict contractual approach|26 2.3.5|Deciding on process models and methods|26 2.3.6|Examples of process models|27 2.3.7|Representation of process models|27 2.4|Quality criteria of process models|29 3.|Current engineering methods for ANNs|30 3.1|Why a special method for ANNs?|30 3.1.1|Are conventional engineering methodologies suitable for ANNs?|30 3.2|Methods for expert systems|31 3.3|System identication methods|35 3.4|Bailey an
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MA-Thesis / Master, die am 01.05.1995 erfolgreich an einer Universität in USA eingereicht wurde. 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. Table of Contents: |List of figures|x |List of tables|xi |Introduction|1 1.|Design attributes in ANN|3 1.1|ANN models|4 1.1.1|Node level|7 1.1.2|Network level|9 1.1.3|Training level|9 1.2|Data and data representation|10 1.3|Global system design|12 1.4|Hardware and software implementation|13 1.5|Characteristics of ANNs|15 1.5.1|Advantages of ANNs|15 1.5.2|Limitations and concerns|16 2.|Technical process models and engineering methods|18 2.1|Why use an engineering method?|18 2.2|Evolutionary model of engineering discipline|20 2.3|Overview of technical process models|22 2.3.1|Taxonomy of technical process models|24 2.3.2|Prototyping|25 2.3.3|Incremental method|26 2.3.4|Strict contractual approach|26 2.3.5|Deciding on process models and methods|26 2.3.6|Examples of process models|27 2.3.7|Representation of process models|27 2.4|Quality criteria of process models|29 3.|Current engineering methods for ANNs|30 3.1|Why a special method for ANNs?|30 3.1.1|Are conventional engineering methodologies suitable for ANNs?|30 3.2|Methods for expert systems|31 3.3|System identication methods|35 3.4|Bailey an
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A Design and Development Method for Artificial Neural Network Projects
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 artifici. N° de réf. du vendeur 5421997
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783838620213
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Taschenbuch. Etat : Neu. 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. N° de réf. du vendeur 9783838620213
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Taschenbuch. Etat : Neu. Neuware -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 [¿]Diplomica Verlag, Hermannstal 119k, 22119 Hamburg 144 pp. Englisch. N° de réf. du vendeur 9783838620213
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Taschenbuch. Etat : Neu. A Design and Development Method for Artificial Neural Network Projects | Stefan Vogt | Taschenbuch | 144 S. | Englisch | 2000 | [.] | EAN 9783838620213 | Verantwortliche Person für die EU: Dryas Verlag, ein Imprint der Bedey und Thoms Media GmbH, Hermannstal 119k, 22119 Hamburg, kontakt[at]dryas[dot]de | Anbieter: preigu. N° de réf. du vendeur 105461933
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