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Information Fusion Management Decision Support System

Author: QiuXue
Tutor: LinJiaJun
School: East China University of Science and Technology
Course: Signal and Information Processing
Keywords: Information Fusion Multi-objective decision Multi-objective Genetic Algorithms Elitist strategy
CLC: TP202
Type: Master's thesis
Year: 2011
Downloads: 106
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Abstract


Multi-sensor information fusion (MSIF) refers to one or more sources from multiple sensors information for analysis, processing , synthesis, resulting in the synthesis of new, more effective process of information . Multi-sensor information fusion with each other through various combinations to achieve fusion algorithm for maneuvering target specific , a combination of different algorithms demonstrated fusion performance varies greatly. This study focuses on information fusion algorithm management system for decision support methods , information fusion algorithm management system for decision support in the hope that through a certain way , so that the system can be based on different characteristics of decision-making maneuvering target motion feedback optimal combination of algorithms to achieve algorithm automatically selects and decision support purposes. Firstly, the traditional method of decision support analysis, pointing out its limitations. Then use a method different from the traditional decision support - multi-objective decision method based on information fusion algorithm for multi-objective decision management system research . Using multi-objective decision making method based on Pareto 's fast non-dominated sorting genetic algorithm (NSGA-Ⅱ), NSGA-Ⅱ algorithm uses the fast non-dominated sorting mechanism , it has the ability to approach problems Pareto front ; while using crowding distance sorting operation , guaranteed to get the Pareto optimal solutions with good diversity. Finally, NSGA-Ⅱ algorithm when generating the sub-population of defects , with the distribution function proposed elitist strategy NSGA-Ⅱ algorithm optimization , the experiment showed that the distribution function with elitist strategy can make the algorithm better population diversity and convergence , and the ability to search out under the same conditions more suitable for maneuvering target specified algorithm combinations .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > General issues > Design, performance analysis and synthesis
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