Recursive approximation by ARX model: A tool for grey box modelling

The presented procedure computes approximate probabilistic models of complex dynamic phenomena recursively with respect to an increasing amount of observed evidence. Measured, fictitious as well as simulated data can be used in combination for obtaining a reasonably conservative approximate model. Thus information from a number of sources can be systematically merged using a refinement of the recently proposed method of Bayesian pooling of imprecise opinions from a variety of experts. It can be applied recursively as the number of treated items grows. The procedure provides (i) a new tool needed for grey as well as black box modelling, (ii) a novel adaptation of probabilistic models and (iii) an approximation of a given model by a simpler one. The general procedure is applied to the autoregressive model with exogenous variables (AM). This example illustrates the adopted approach and conmbutes to the solution of the following tasks: (i) estimation of an appropriate model structure; (ii) incorporation of prior knowledge into the initial conditions of recursive least squares; (iii) construction of a reference for an advanced forgetting technique; (iv) approximation of a complex analytic/simulation model by an ARX model. The behaviour of the procedure is illustrated on typical examples.     

Experiment design for grey box identification

Grey box models are characterized by their physical significance e.g. in parametrization and by the partial prior information that is available about e.g. the parameter values. These aspects of the grey box model affect the design of optimal excitations for identification and we study the extension of classical theory for experiment design to input design for identification of grey box models. Partial prior information is expressed as a probability distribution and is employed in the design of optimal excitations through optimization of Bayesian criteria.     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

2014电子测量仪器产品调查暨年度特殊贡献产品评选活动圆满完成

《国外电子测量技术》(以下简称"FEMT")杂志社主办的"2014电子测量仪器产品调查暨年度特殊贡献产品评选活动"(以下简称"调查活动")于2015年01月29日圆满完成。本次"调查活动"得到了中国电子学会电子测量与仪器分会,中国仪器仪表学会电子测量与仪器分会、虚拟仪器与网络化系统分会以及中电会展公司、中国电子仪器行业协会、北京市电子科技情报研究所等众多单位的大力支持,并得到了《仪器仪表学     

专注于创新，而非集成

我们可以将来自不同供应商的产品和定制硬件拼凑成一个复杂的系统,但代价是什么?当下一个非预期升级来临时,比如某个零件生命周期结束(EOL)或客户提出新需求,控制工程师就会面临着一系列新的相似问题。如果某个关键零件确实面临EOL或者供应商决定改变其通信协议,整个系统将处于被淘汰的风险。这种情况发生时,控制工程师每次都需要根据系统的小变化解决相似的挑战。此外,创建和维护这样的系统需要多     

基于灰箱理论的配电房温度变化预测模型研究

配电房环境封闭、透风性差、温度较高,为解决因温度高而影响电力设备安全运行的问题,需要研究配电房温度变化的预测模型,以便进行温度调控。提出基于灰箱理论研究的配电房温度变化的预测模型,为实现配电房温度的自动化控制提供了模型基础。首先介绍了配电房的基本结构,然后根据传热学、动力学等物理知识及热电相似理论得到配电房的热电模型;接下来基于灰箱法利用相关数据的时间序列估计该模型中未知的物理参数,并通过其自相关矩阵验证模型的合理性;最后,将模型的一步和六步预测温度值和实际温度值进行对比分析,结果表明该模型能够较好地描述配电房的热动力学特性,与配电房的实际温度变化相一致。     

Special issue on the 100th anniversary of Xiamen University

This special issue is devoted to the celebration of the century anniversary of Xiamen University (XMU) (6 April 2021) and the establishment of the LSA Editorial...