基于全新融合机制的模糊认知图集成分类器模型与算法研究

项目名称： 基于全新融合机制的模糊认知图集成分类器模型与算法研究

项目编号： No.61300078

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 马楠

作者单位： 北京联合大学

项目金额： 22万元

中文摘要： 本研究突破国内外模糊认知图(Fuzzy Cognitive Map ,FCM)集成分类器研究局限于推理机制和学习方法框架的现状，另辟了以机理研究为核心、构造其模型与算法的新路经，以适应在复杂、动态系统中动力学分析等研究领域的需要。主要研究内容如下 ：(1)基于启发协调机制，解决模糊认知图分类器学习过程中知识短缺瓶颈；(2) 基于量子进化机制提出全新的量子遗传态下和混沌量子遗传态下模糊认知图分类器算法，以解决其关键技术中的分类速度问题； (3) 基于协同演化机制，并与前二机制融合构造模糊认知图集成分类器模型，以解决各基本分类器集成精度难以提高这一核心技术难题。将以上模型与算法应用于生物信息学领域挑战性难题---蛋白质二级结构预测中。经大量文献的跟踪与查新，证实该项研究线路在国内外尚属首次，且是此前承担国家自然科学基金项目成果的拓展，形成了一个研究系列。

中文关键词： 模糊认知图；量子遗传；集成分类器；蛋白质二级结构；

英文摘要： This project is to break through the limitations of FCM inference mechanism and learning methods which is difficult in fully implementing its' expression skill and strong reasoning ability. Furthermore, we aim to open a new research path which seems the mechanism construction as the core companied with the building the model and algorithm to adapt to the complex and dynamic systems. Based on our former study, the object of this project includes (1) use the heuristic coordinator to solve the knowledge shortage bottleneck in the learning process for fuzzy cognitive map classifier;(2) then put forward to a quantum genetic mechanism and then chaotic quantum based fuzzy cognitive map ensemble classifiers model, to speed up the classification speed. 3) Finial, we will construct a collaborative evolutionary mechanism based fuzzy cognitive map ensemble classifier model, trying to solve the accuracy bottleneck of the basic classifier. All the contributions will be applied to a challenging problem in the field of bioinformatics, protein secondary structure prediction, to test its effectiveness and progressiveness.According to a large number of documents, it is confirmed that theresearch line is proposed for the first time at home and abroad, following our previousNational Natural Science Foundation of China, and they prob

英文关键词： fuzzy cognitive map；quantum genetic；ensemble classifier；protein secondary structure；