Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory

Study of Evolutionary Computation and Rough Sets Theory and the Application in Image Processing

Author QiuYuXia
Tutor XieKeMing
School Taiyuan University of Technology
Course Circuits and Systems
Keywords evolutionary computation rough sets theory mind evolutionary algorithm population fitness chain population evolutionary entropy image processing
CLC TP18
Type PhD thesis
Year 2007
Downloads 719
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Computing Intelligence (CI) methods not only have the property of self-study, self-organizing, self-adaptive, but also is excellent for simple, current, strong robust and available for parallel process. Hereby, CI has been widely applied in parallel search, associateon memory, pattern recognition, knowledge automatic acquiring etc. Evolutionary Computing (EC) and Rough Sets Theory (RST) are the two hot directions for CI research and vest in the cross-frontier research area of the information science, the automation science and the computer science. Mind Evolutionary Algorithm (MEA) is a new kind of EC algorithm simulating the evolutionary course of human mind. This paper is based on the study of EC and RST and composed of four parts. First, study on numeral sequence model of EC and its application in convergency analysis. Second, study on Mind Evolutionary Algorithm Based on Population Entropy (PEMEA). Third, study on the decision rules reduct strategy in RST based on bit coded discemibility matrix. Fourth, study on digital image processing method based on PEMEA and RST.Specifically, including the following sections:1. Propose a numeral sequence model for evolutionary computation. A mapping between the evolutionary process of population and a numeral sequence consisted with population fitness function value is built. Thus the sequence reflects the status of the population in evolution. Then, the convergency of several typical evolutionary algorithms analyzed with the model.2. Study the evolutionary mechanism and then introduce the concept of entropy into the design of MEA. A new MEA based on population evolutionary entropy is presented and its convergency is proved under the uniform frame of EC. Then numeral optimal experiment is done to analyze the algorithmic performance.3. Present a decision-rule extraction algorithm based on bit-coded discernibility matrix in RST. At last, it is successfully used to extract the decision rules for course operation of cement kiln. 4. Present new methods of image processing using PEMEA and RST. PEMEA is employed to optimize the segmentation threshold and then the image is segmented with the optimal threshold. A form classification system is built with decision table in RST and the decision-rule extraction algorithm based on bit-coded discernibility matrix is used to reduct the decision rules. At last, an analysis system for chromosome aberrance is designed based on the two methods.The innovations of this paper are as follows:(1) Propose a numeral sequence model for EC. The complex random process of population evolution is mapped to a numeral sequence. Thus some properties are figured out by analyzing the sequence. That provides a new method for the theory research on EC.(2) Employ the numeral sequence model of EC to analyze the convergency of several typical evolutionary algorithms. The convergence condition with population fitness chain is presented. Some theorems in functions analysis such as interval sheath theorem are used in the proof of their convergency.(3) Present a new mind evolution algorithm based on population evolution entropy (PEMEA). The thinking of information entropy in information theory is introduced in the design of evolutionary operation for MEA. With the improved ’similartax’ operator, the algorithm estimates the population evolutionary entropy and then automatically adjusts the control parameters accordingly. The self-adaptive search strategy improves the algorithmic efficiency.(4) Present an extraction strategy based on bit-coded discernibility matrix. Firstly, the rationality of decision rules reduction with discemibility matrix is analyzed. Then a new decision-rule extraction algorithm is presented. The algorithm is based on bit-coded discemibility matrix, performing the reduction of both attributes and attributes’ value. At last, it is successfully used to extract the decision rules for course operation of cement kiln.(5) Respectively apply PEMEA and RST to image segmentation and image analysis. Design an analysis system for chromosome aberrance based on PEMEA and RST.

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