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Application of Genetic Algorithm Based Neural Network in Handwritten Digits Recognition

Author: JiangWei
Tutor: GuZhongZuo
School: Sichuan University
Course: Pattern Recognition and Intelligent Systems
Keywords: Neural Network BP Algorithm Genetic Algorithm Handwritten Digits Recognition
CLC: TP391.41
Type: Master's thesis
Year: 2003
Downloads: 210
Quote: 2
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Abstract


Handwritten digits recognition is one branch of optical character recognition (OCR), which has broad potential application in fields of postal service, banking, finance and pattern recognition theory research.Artificial neural network originates from human’s imitation to self. The mathematical model of neural cell was presented in 1943 and the following advance of learning algorithm encouraged the research of neural network. Backpropagation (BP) learning algorithm is commonly used in practical use because of its simple and efficiency.Genetic Algorithm (GA) is a kind of parallel and efficient search algorithm for global optimal solution, which is based on Darwin’s natural evolution theory and Mendel’s genetic mutation theory. GA has the ability of automatic acquiring and storing knowledge of search space in the process of searching optimal solution and can adaptively control the search process to approach the optimal solution.In this thesis, the author based on the basic theory of neural network and genetic algorithm and applied genetic algorithm based neural network to handwritten digits recognition. Seeing that BP neural network may easily get struck into local optimal solution and is dependent on the initial weights, the author proposed to replace the BP learning algorithm with genetic algorithm to train the neural network and then compared this two algorithms in the application of handwritten digits recognition.Experimental result shows that the genetic algorithm based neural network is efficient for handwritten digits recognition, because this method can avoid to be struck into local optimal solution, does not dependent on initial weights and can also achieve high recognition rate.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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