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Artificial intelligence : approaches, tools, and applications / Brent M. Gordon, editor.

Contributor(s): Material type: TextTextSeries: Scientific revolutions series | Computer science, technology and applicationsPublisher: New York : Nova Science Publishers, [2011]Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781620814857
  • 1620814854
Subject(s): Genre/Form: Additional physical formats: Print version:: Artificial intelligenceDDC classification:
  • 006.3 22
LOC classification:
  • Q335.5
Online resources:
Contents:
PREFACE ; APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE UPSTREAM OIL AND GAS INDUSTRY ; ABSTRACT ; 1. NEURAL NETWORKS AND THEIR BACKGROUND ; 1.1. A Short History of Neural Networks ; 1.2. Structure of a Neural Network ; 1.3. Mechanics of Neural Networks Operation ; 2. EVOLUTIONARY COMPUTING ; 2.1. Genetic Algorithms ; 2.2. Mechanism of a Genetic Algorithm ; 3. FUZZY LOGIC ; 3.1. Fuzzy Set Theory ; 3.2. Approximate Reasoning ; 3.3. Fuzzy Inference ; 4. APPLICATIONS IN THE OIL AND GAS INDUSTRY ; 4.1. Neural Networks Applications.
4.1.1. Reservoir Characterization 4.1.2. Virtual Magnetic Resonance Imaging Logs ; 4.2. Genetic Algorithms Applications ; 4.3. Fuzzy Logic Applications ; 4.3.1. Results ; REFERENCES ; AN ARTIFICIAL INTELLIGENCE APPROACH FOR MODELING AND OPTIMIZATION OF THE EFFECT OF LASER MARKING PARAMETERS ON GLOSS OF THE LASER MARKED GOLD ; ABSTRACT ; 1. INTRODUCTION ; 2. ANFIS, ANNS, GA AND PSO ; 2.1. Adaptive Neuro-Fuzzy Inference System ; 2.1.1. Anfis Architecture ; 2.1.2. ANFIS Learning Algorithm ; 2.2. Artificial Neural Networks ; 2.2.1. Network Types ; 2.2.2. Training Algorithm.
2.3. Genetic Algorithm (a) Population Initialization ; (b) Operators ; (c) Chromosome Evaluation ; 2.4. Particle Swarm Optimization ; 3. INPUT/OUTPUT VARIABLES ; 4. ANFIS AND ANNSIMPLEMENTATION ; 4.1. Model Building Methodology ; 4.2. ANFIS Modeling ; 4.3. ANNs Modeling ; 4.4. Results and Discussion ; 5. GA AND PSO IMPLEMENTATION ; 5.1. Optimization ; 5.2. Optimization Using GA ; 5.3. Optimization Using PSO ; 5.4. Results and Discussion ; 6. METHODOLOGY VALIDATION ; CONCLUSION ; APPENDIX A. COMPARISONOF SOMEOF ANFIS MODELING AND ANN MODELINGRESULTSBEFORE AND AFTERCLEANING THE DATA.
3. PROCEDURE FOR DEVELOPMENT OF KNOWLEDGE BASE SYSTEM (KBS) FOR DESIGN OF METAL STAMPING DIE 3.1. Knowledge Acquisition ; Literature Reviews ; Die Design Experts ; Industrial Visits ; Industrial Brochures ; 3.2. Framing of Production Rules ; 3.3. Verification of Production Rules ; 3.4. Sequencing of Production Rules ; 3.5. Identification of Suitable Hardware and a Computer Language ; 3.6. Construction of Knowledge Base ; 3.7. Choice of Search Strategy ; 3.8. Preparation of User Interface ; 4. AN INTELLIGENT SYSTEM FOR DESIGN OF PROGRESSIVE DIE: INTPDIE.
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Includes bibliographical references and index.

Description based on print version record.

English.

PREFACE ; APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE UPSTREAM OIL AND GAS INDUSTRY ; ABSTRACT ; 1. NEURAL NETWORKS AND THEIR BACKGROUND ; 1.1. A Short History of Neural Networks ; 1.2. Structure of a Neural Network ; 1.3. Mechanics of Neural Networks Operation ; 2. EVOLUTIONARY COMPUTING ; 2.1. Genetic Algorithms ; 2.2. Mechanism of a Genetic Algorithm ; 3. FUZZY LOGIC ; 3.1. Fuzzy Set Theory ; 3.2. Approximate Reasoning ; 3.3. Fuzzy Inference ; 4. APPLICATIONS IN THE OIL AND GAS INDUSTRY ; 4.1. Neural Networks Applications.

4.1.1. Reservoir Characterization 4.1.2. Virtual Magnetic Resonance Imaging Logs ; 4.2. Genetic Algorithms Applications ; 4.3. Fuzzy Logic Applications ; 4.3.1. Results ; REFERENCES ; AN ARTIFICIAL INTELLIGENCE APPROACH FOR MODELING AND OPTIMIZATION OF THE EFFECT OF LASER MARKING PARAMETERS ON GLOSS OF THE LASER MARKED GOLD ; ABSTRACT ; 1. INTRODUCTION ; 2. ANFIS, ANNS, GA AND PSO ; 2.1. Adaptive Neuro-Fuzzy Inference System ; 2.1.1. Anfis Architecture ; 2.1.2. ANFIS Learning Algorithm ; 2.2. Artificial Neural Networks ; 2.2.1. Network Types ; 2.2.2. Training Algorithm.

2.3. Genetic Algorithm (a) Population Initialization ; (b) Operators ; (c) Chromosome Evaluation ; 2.4. Particle Swarm Optimization ; 3. INPUT/OUTPUT VARIABLES ; 4. ANFIS AND ANNSIMPLEMENTATION ; 4.1. Model Building Methodology ; 4.2. ANFIS Modeling ; 4.3. ANNs Modeling ; 4.4. Results and Discussion ; 5. GA AND PSO IMPLEMENTATION ; 5.1. Optimization ; 5.2. Optimization Using GA ; 5.3. Optimization Using PSO ; 5.4. Results and Discussion ; 6. METHODOLOGY VALIDATION ; CONCLUSION ; APPENDIX A. COMPARISONOF SOMEOF ANFIS MODELING AND ANN MODELINGRESULTSBEFORE AND AFTERCLEANING THE DATA.

3. PROCEDURE FOR DEVELOPMENT OF KNOWLEDGE BASE SYSTEM (KBS) FOR DESIGN OF METAL STAMPING DIE 3.1. Knowledge Acquisition ; Literature Reviews ; Die Design Experts ; Industrial Visits ; Industrial Brochures ; 3.2. Framing of Production Rules ; 3.3. Verification of Production Rules ; 3.4. Sequencing of Production Rules ; 3.5. Identification of Suitable Hardware and a Computer Language ; 3.6. Construction of Knowledge Base ; 3.7. Choice of Search Strategy ; 3.8. Preparation of User Interface ; 4. AN INTELLIGENT SYSTEM FOR DESIGN OF PROGRESSIVE DIE: INTPDIE.

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