Using Genetic Search to Refine Knowledge-Based Neural Networks

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

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Integration of Neural Networks and knowledge-based systems in medicine

Artificial Intelligence in Medicine - Lecture Notes in Computer Science ◽

10.1007/3-540-60025-6_170 ◽

1995 ◽

pp. 425-426 ◽

Cited By ~ 10

Author(s):

A. Ultsch ◽

D. Korus ◽

T. O. Kleine

Keyword(s):

Neural Networks ◽

Knowledge Based Systems ◽

Knowledge Based

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Artificial Intelligence in Software Engineering

Machine Learning ◽

10.4018/978-1-60960-818-7.ch504 ◽

2012 ◽

pp. 1215-1236 ◽

Cited By ~ 2

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.

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Artificial Intelligence in Software Engineering

Advances in Computational Intelligence and Robotics - Artificial Intelligence Applications for Improved Software Engineering Development ◽

10.4018/978-1-60566-758-4.ch014 ◽

2010 ◽

pp. 278-299 ◽

Cited By ~ 7

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.

Download Full-text