基于量子学习和调节网络的多目标聚类方法研究

项目名称： 基于量子学习和调节网络的多目标聚类方法研究

项目编号： No.61272279

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 李阳阳

作者单位： 西安电子科技大学

项目金额： 61万元

中文摘要： 随着信息获取与处理技术的飞速发展，如何实现高效的复杂数据感知与识别成为热点。本课题针对进化算法在处理复杂分布大样本数据聚类时缺乏结构逼近和学习能力弱，从而借鉴神经调节机理，构造具有容噪、泛化和记忆能力的调节网络模型；将量子并行计算机理引入到多目标进化，为解决调节网络高维复杂的结构特点，建立量子多目标学习算法，优化网络结构和参数，实现高效学习；将聚类看作为一个优化过程，克服单目标优化处理复杂据聚类时存在的聚类参数难以确定、要求数据分布单一、抗噪能力差等瓶颈问题，采用多目标优化框架，增强聚类方法的鲁棒性和普适性，建立基于量子学习和调节网络计算模型的多目标聚类算法，分析并解决新模型与学习算法在求解复杂分布和大样本数据聚类中的关键问题，包括量子并行策略设计及聚类有效准侧设计，多目标学习框架构造，计算复杂度分析等；用具有挑战性的遥感图像分割和变化检测问题，来验证该网络计算模型的有效性与可扩展性。

中文关键词： 量子计算；调节网络；多目标优化；网络信息处理；图像理解

英文摘要： With the rapid development of information acquisition and information processing technology, it has been a hot topic that how to achieve high-efficiency complex data perception and recognition. On account of the problems that the deficiency of structure approximation and weak learning ability when evolutionary algorithms dealing with complicated distribution and large sample data clustering. With reference to neural regulation mechanism, this subject constructs the regulation network model with the ability of noise accommodation, generalization and keeping memory. To solve the structure characteristic of high dimension and complexity in regulation network, this subject introduces quantum parallel computation into multiobjective optimization, and forms quantum-inspired multiobjective learning algorithm, and then optimizes network structure and parameters followed by high-efficiency learning. In this subject, clustering is regarded as an optimization problem, and in order to overcome the bottleneck problems that difficulty in determining clustering parameters, the requirement of sole data distribution and bad noise immunity in single objective optimization to process complicated data clustering, multiobjective optimization framework is adopted in this subject to strengthen the robustness and universality of clust

英文关键词： Quantum computation；regulation network；multiobjective optimization；network information processing；image understanding