基于多维泰勒网的超前d步预测模型
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作者单位:

(1. 东南大学 自动化学院,南京 210096;2. 东南大学 复杂工程系统测量与控制教育部重点实验室,南京 210096)

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E-mail: hsyan@seu.edu.cn.

中图分类号:

TM351

基金项目:

国家自然科学基金项目(61673112,60934008);中央高校基本科研业务费专项资金项目(2242017K10003, 2242014K10031);江苏省高校优势学科建设工程项目;江苏省研究生培养科研创新工程项目(KYCX18_ 0100).


d-step-ahead predictive model based on multi-dimensional Taylor network
Author:
Affiliation:

(1.School of Automation,Southeast University,Nanjing210096,China;2.MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering,Southeast University,Nanjing210096,China)

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    摘要:

    针对单入单出(SISO)与多入多出(MIMO)非线性时滞系统构建预测模型准确性问题,分别提出基于多维泰勒网(MTN)的预测模型构建方案.首先,分别依靠非递推技术与递推技术来设计非递推d步与递推d步超前MTN预测模型,给出二者表达式,二者皆可对未来d步范围进行预测,并有效弥补时滞带来的影响;然后,利用阻尼递推最小二乘(DRLS)算法,带有动量因子的BP算法,Levenberg Marquardt(L-M)算法和扩展卡尔曼滤波(EKF)算法分别作为MTN预测模型的学习算法进行实时在线学习;最后,引入两个仿真例子来验证所建立预测模型的准确性和实时性,并与神经网络预测模型作对比.实验结果表明,相比较神经网络预测模型,所提出的两种在线构建预测模型方案具有更好的准确性与实时性.同时,4种不同的学习算法对MTN预测模型的准确度影响不大.

    Abstract:

    For the accuracy of the predictive models for single input single output (SISO) and multiple input multiple output (MIMO) nonlinear time-delay systems, the schemes of constructing predictive models based on the multi-dimensional Taylor network (MTN) are proposed. First, the non-recursive direct d-step-ahead MTN predictive model and the recursive d-step-ahead MTN predictive model are designed and the expressions are given respectively relying on non-recursive and recursive technology, which can predict the range of d steps in the future and compensate the influence of time-delay. Then the damped recursive least squares (DRLS), the BP algorithm with momentum factors, the Levenberg Marquardt (L-M) and the extended Kalman filter (EKF) learning algorithms are used as the learning of the MTN predictive model to conduct on-line learning, respectively. Finally, two experimental examples are given to certify the accuracy and real-time performance of the predictive model, which are compared with those of the neural network predictive models. Results from our experiments show that the two kinds of predictive models constuctedusing the proposed schemes have a better accuracy and real-time performance than the neural network predictive models. Meanwhile, four kinds of different learning algorithms have little effect on the accuracy of the MTN predictive models.

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李晨龙,严洪森.基于多维泰勒网的超前d步预测模型[J].控制与决策,2021,36(2):345-354

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  • 在线发布日期: 2021-01-21
  • 出版日期: 2021-02-20