Intelligent Computation for Manufacturing

2015 ◽  
pp. 1748-1782
Author(s):  
Ashraf Afify
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Inductive Learning ◽  
Knowledge Based Systems ◽  
Computation Techniques ◽  
Practical Applications ◽  
Knowledge Based

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.

Intelligent Computation for Manufacturing

2013 ◽  
pp. 211-246 ◽  
Author(s):  
Ashraf Afify
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Inductive Learning ◽  
Knowledge Based Systems ◽  
Computation Techniques ◽  
Practical Applications ◽  
Knowledge Based

Intelligent computation refers to intelligence artificially realised through computation. This chapter reviews six intelligent computation techniques. They are: knowledge-based systems, fuzzy logic, inductive learning, neural networks, genetic algorithms, and swarm intelligent techniques. All of these tools have found many practical applications. Examples of applications in manufacturing are given in the chapter.

Towards intelligent systems for boiler design

1997 ◽  
Vol 211 (5) ◽  
pp. 399-409 ◽  
Author(s):  
S Lu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Knowledge Based Systems ◽  
Design Practice ◽  
Knowledge Based ◽  
Computer Aided ◽  
Boiler Design

This paper describes the application, through examples and comparisons, of artificial intelligence including neural networks, fuzzy logic, genetic algorithms in three levels of computer aided boiler design: design by mathematical modelling, design by optimization and design by knowledge-based systems. It reviews the state-of-the-art situation and trends for future development in boiler design practice.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Artificial Intelligence in Software Engineering

2012 ◽  
pp. 1215-1236 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

Artificial Intelligence in Software Engineering

2010 ◽  
pp. 278-299 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

Intelligent Hybrid System: A Reliability-Based Failure Management Application

2001 ◽  
Vol 5 (6) ◽  
pp. 307-314 ◽  
Author(s):  
Jose Aguilar ◽  
◽  
Mariela Cerrad ◽  
Katiuska Morillo ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Historical Data ◽  
Intelligent System ◽  
The Other ◽  
Hybrid Architecture ◽  
System A ◽  
Reliability Centered Maintenance ◽  
Management Application

The integration of different intelligent techniques (such as Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, etc.) into a hybrid architecture allows to overcome their individual limitations. In industrial environments, these intelligent techniques can be combined to reach more effective solutions to complex problems. On the other hand, failure management in processes, equipment or plants, acquires more importance in modern industry every day, in order to minimize unexpected faults and guaranties a greater reliability, safety, disposition and productivity in the industry. In this paper, an intelligent system is designed for failure management based on Reliability Centered Maintenance methodology, Fuzzy Logic and Neural Networks. The system proposes the maintenance tasks according to the historical data of the equipment.

scholarly journals Guest Editorial

1997 ◽  
Vol 12 (3) ◽  
pp. 229-230 ◽  
Author(s):  
DAVE STUART ROBERTSON
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
World Wide ◽  
Guest Editorial ◽  
Human Life ◽  
Knowledge Based Systems ◽  
Fuzzy Logic Controllers ◽  
Incorrect Decision ◽  
Knowledge Based ◽  
The World

Knowledge based systems are used in applications where an incorrect decision could put human life in jeopardy. A quick trawl through the World Wide Web is sufficient, these days, to locate such applications in design, analysis and testing; protection advice; operator decision support; signal monitoring; embedded systems and others. Depending on the type of system, these either give information which is not guaranteed to be correct (in many operator support applications) or which is imprecise (for example in fuzzy logic controllers).

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