面向综合力学环境预测的回归多任务学习研究

项目名称： 面向综合力学环境预测的回归多任务学习研究

项目编号： No.U1204609

项目类型： 联合基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 毛文涛

作者单位： 河南师范大学

项目金额： 31万元

中文摘要： 为降低工程代价和风险，综合力学环境预测期望从试验数据对复杂力学环境进行预测，已成为航空航天、机械制造等复杂工业领域的关键工程环节。然而，如何从少量试验数据对复杂力学环境进行准确而鲁棒的预测依然是困扰该行业的难点。为解决该问题，与以往对单体力学对象进行测试和建模的方法不同，本项目拟采用多个相似（而不是相同）力学对象进行辅助建模，着重对回归多任务学习理论和算法进行研究。首先，本项目拟引入覆盖数理论，建立回归多任务学习泛化误差界及其与任务样本数、目标/辅助任务样本数比值的函数关系。在此基础上，针对不同需求，本项目拟采用多输出支持向量机作为基础算法，构建新的回归多任务学习算法和相应的模型选择方法，并提出一种新的任务聚类算法和不对称多任务学习算法。最后，将建立圆柱壳力学环境试验系统，对上述算法的实际效果进行评价。研究成果可有效提高对复杂力学环境预测的精度和数值稳定性，具有重要的理论和工程意义。

中文关键词： 多任务学习；力学环境预测；回归学习；支持向量机；极限学习机

英文摘要： Aiming at reducing engineering cost and risk, the prediction of combined dynamic environment which roots on experimental data has become a key issue in the complex industrial fields of aeronautics and astronautics, mechanical manufacture and so on. However, it is still difficult to obtain accurate and robust predictive results of complex dynamic environment merely from a small number of experimental data. To solve this problem, different from the current approach which utilizes single mechanical object for testing and modeling, this project presents a novel multi-task learning based idea which plans to establish the prediction model by adopting multiple similar but not identical mechanical objects. This project focuses on the research on multi-task learning theory and algorithm. First, this project plans to introduce covering number theory to establish the generalization bound of regression multi-task learning machine, and the functional relationship between this bound and sample number as well as the ration of target/auxiliary tasks’ number. Based on this analysis, according to different engineering demands, this project plans to adopt multi-dimensional support vector machine as basic algorithm, and further construct a new regression multi-task learning machine and its model selection method. Moreover, a new ta

英文关键词： multi-task learning；dynamic environment prediction；regression learning；support vector machine；extreme learning machine