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HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING

YOUSSIF, ROSHDY S.
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219

Abstract Details

2004, PhD, University of Cincinnati, Engineering : Computer Science and Engineering.
Hybrid Intelligent Systems (HIS) combine intelligent techniques in synergistic architectures in order to provide solutions for complex problems. These systems utilize at least two of the three techniques: fuzzy logic, genetic algorithms and neural networks. The goal of their combination is to amplify their strengths and complement their weaknesses. Research in hybrid intelligent systems primarily focuses on the integration and interaction of different techniques rather than merging different methods to create new techniques. However, it is not always obvious or easy to build HIS architectures that achieve the higher intelligence goal. A good architecture for a hybrid system should match each of its tasks to the appropriate intelligent technique and provide an efficient means for their integration. Classification of signal patterns represents a complex problem due to the voluminous nature of signal patterns. A signal pattern is the combination of a large sequence of values of one or more variables collected over a period of time. Noise is an intrinsic component to all signal pattern applications. Current classification methods are inadequate in classifying large sets of noisy signal patterns. We selected this problem as the target for our new HIS architecture. In this research we developed a new HIS architecture for classifying large sets of signal patterns. Our Hybrid Intelligent Signal Pattern Classifier (HISPC) has demonstrated superior performance with low classification cost and great flexibility on synthetic and real life large data sets. An equally important objective of this research is the study of the software engineering aspect for developing this architecture. A new software engineering process and a set of design and implementation principles were developed in this work. These tools are applicable to the development of any experimental software system.
Dr. Carla Purdy (Advisor)
188 p.
Computer Science
Hybrid intelligent systems; Pattern classification; Signal patterns; Experimental software systems

Recommended Citations

Citations

  • YOUSSIF, R. S. (2004). HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219

    APA Style (7th edition)

  • YOUSSIF, ROSHDY. HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING. 2004. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219.

    MLA Style (8th edition)

  • YOUSSIF, ROSHDY. "HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING." Doctoral dissertation, University of Cincinnati, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219

    Chicago Manual of Style (17th edition)

ucin1085714219
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© 2004, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.