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Intelligent systems using GMDH algorithms

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Date Issued:
2010
Summary:
Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
Title: Intelligent systems using GMDH algorithms.
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Name(s): Gupta, Mukul.
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2010
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: ix, 63 p. : ill. (some col.)
Language(s): English
Summary: Design of intelligent systems that can learn from the environment and adapt to the change in the environment has been pursued by many researchers in this age of information technology. The Group Method of Data Handling (GMDH) algorithm to be implemented is a multilayered neural network. Neural network consists of neurons which use information acquired in training to deduce relationships in order to predict future responses. Most software tool during the simulation of the neural network based algorithms in a sequential, single processor machine like Pascal, C or C++ takes several hours or even days. But in this thesis, the GMDH algorithm was modified and implemented into a software tool written in Verilog HDL and tested with specific application (XOR) to make the simulation faster. The purpose of the development of this tool is also to keep it general enough so that it can have a wide range of uses, but robust enough that it can give accurate results for all of those uses. Most of the applications of neural networks are basically software simulations of the algorithms only but in this thesis the hardware design is also developed of the algorithm so that it can be easily implemented on hardware using Field Programmable Gate Array (FPGA) type devices. The design is small enough to require a minimum amount of memory, circuit space, and propagation delay.
Identifier: 702127110 (oclc), 2976442 (digitool), FADT2976442 (IID), fau:3577 (fedora)
Note(s): by Mukul Gupta.
Thesis (M.S.C.S.)--Florida Atlantic University, 2010.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.
Subject(s): GMDH algorithms
Genetic algorithms
Pattern recognition systems
Expert systems (Computer science)
Neural networks (Computer science)
Fuzzy logic
Intelligent control systems
Persistent Link to This Record: http://purl.flvc.org/FAU/2976442
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU