A Design and Development Method for Artificial Neural Network Projects par Stefan Vogt: New (1995)

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Titre: A Design and Development Method for Artificial Neural Network Projects
�diteur: Diplomarbeiten Agentur diplom.de
Date de parution: 1995
Langue: anglais
ISBN � 10 chiffres: 3838620216
ISBN � 13 chiffres: 9783838620213
Reliure: Couverture souple
�tat de l'article: New

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Synopsis

MA-Thesis / Master, die am 01.05.1995 erfolgreich an einer Universit�t 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 Universit�t 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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