Identification Systems

Basu, S., Kumar, A. and Lawama, K.K., System identification of a circuit breaker pole . Proceedings of Tenth Symposium on Earthquake Engineering, Roorkee, pp. 979 9 (1994). 

Ljung, L., 1987. System Identification Theory for the user. New York Prentice-Hall. 

The identification of plant models has traditionally been done in the open-loop mode. The desire to minimize the production of the off-spec product during an open-loop identification test and to avoid the unstable open-loop dynamics of certain systems has increased the need to develop methodologies suitable for the system identification. Open-loop identification techniques are not directly applicable to closed-loop data due to correlation between process input (i.e., controller output) and unmeasured disturbances. Based on Prediction Error Method (PEM), several closed-loop identification methods have been presented Direct, Indirect, Joint Input-Output, and Two-Step Methods. 

The PBL reactor considered in the present study is a typical batch process and the open-loop test is inadequate to identify the process. We employed a closed-loop subspace identification method. This method identifies the linear state-space model using high order ARX model. To apply the linear system identification method to the PBL reactor, we first divide a single batch into several sections according to the injection time of initiators, changes of the reactant temperature and changes of the setpoint profile, etc. Each section is assumed to be linear. The initial state values for each section should be computed in advance. The linear state models obtained for each section were evaluated through numerical simulations. 

N4SID = numerical algorithms for subspace state space system identification t = time [sec]... 

D-TOCSY-NOESY Sequential assignment of the spin systems, identification of nOes... 

Multiscale process identification and control. Most of the insightful analytical results in systems identification and control have been derived in the frequency domain. The design and implementation, though, of identification and control algorithms occurs in the time domain, where little of the analytical results in truly operational. The time-frequency decomposition of process models would seem to offer a natural bridge, which would allow the use of analytical results in the time-domain deployment of multiscale, model-based estimation and control. 

EPA. 1980b. Hazardous waste management system Identification and listing of hazardous waste Interim rule. U.S. Environmental Protection Agency. Federal Register 45 78530-78550. 

The very basis of the kinetic model is the reaction network, i.e. the stoichiometry of the system. Identification of the reaction network for complex systems may require extensive laboratory investigation. Although complex stoichiometric models, describing elementary steps in detail, are the most appropriate for kinetic modelling, the development of such models is time-consuming and may prove uneconomical. Moreover, in fine chemicals manufacture, very often some components cannot be analysed or not with sufficient accuracy. In most cases, only data for key reactants, major products and some by-products are available. Some components of the reaction mixture must be lumped into pseudocomponents, sometimes with an ill-defined chemical formula. Obviously, methods are needed that allow the development of simple... 

M. Norgaard, Neural network based system identification toolbox. Technical report. Institute of Automation, Technical University, Denmark, 1995. 

The design procedures depend heavily on the dynamic model of the process to be controlled. In more advanced model-based control systems, the action taken by the controller actually depends on the model. Under circumstances where we do not have a precise model, we perform our analysis with approximate models. This is the basis of a field called "system identification and parameter estimation." Physical insight that we may acquire in the act of model building is invaluable in problem solving. 

Eykhoff, P. System Identification. Wiley-Interscience, New York (1974). 

Maria, G. and Heinzle, E. (1998) Kinetic System Identification by Using Short-Cut Techniques in Early Safety Assessment of Chemical Processes, J. Loss Prev. Process Ind. 11, 187-206. 

Robust system identification and estimation has been an important area of research since the 1990s in order to get more advanced and robust identification and estimation schemes, but it is still in its initial stages compared with the classical identification and estimation methods (Wu and Cinar, 1996). With the classical approach we assume that the measurement errors follow a certain statistical distribution, and all statistical inferences are based on that distribution. However, departures from all ideal distributions, such as outliers, can invalidate these inferences. In robust statistics, rather than assuming an ideal distribution, we construct an estimator that will give unbiased results in the presence of this ideal distribution, but will be insensitive to deviation from ideality to a certain degree (Alburquerque and Biegler, 1996). 

Wu, X., and Cinar, A. (1996). An adaptive robust M-estimator for nonparametric nonlinear system identification. J. Proc. Control 6, 233-239. 

In formulating a model of a very large process such as a whole chemical plant, the possibility exists that a subset of the system equations does not contain any variables in common with the remaining equations in the system. Such a subset of equations may physically correspond to a process unit or group of process units that are not connected in any way to the remaining units in the process. If this situation occurs, the subset of equations, which is called a disjoint subsystem, can be solved completely independently of the remaining equations in the system. Identification of these disjoint subsystems reduces the dimensionality of the complete system to that of the largest disjoint subsystem. 

There are several computer software packages that are quite helpful in applying some of the computationally intensive methods. The PC-MATLAB System Identification Toolbox (The Math Works, Inc., Sherborn, Mass.) is an easy-to-use, powerful software package that provides an array of alternative tools,... 

A very popular sequence of inputs is the pseudorandom binary sequence (PRBS). It is easy to generate and has some attractive statistical properties. See System Identification For Self-Adaptive Control, W. D. T. Davies, London, Wiley-Iflterscience, 1970. 

Several method performance indicators are tracked, monitored and recorded. Items that are recorded include the date of analysis, identification of the HPLC system, identification of the analyst, number and type of samples analyzed, the system precision, the critical resolution or... 

Barth, H., Roebling, R., Fritz, M. and Aktories, K., The binary Clostridium botulinum C2 toxin as a protein delivery systems. Identification of the minimal protein region necessary for interaction of toxin, J. Biol. Chem., 277, 5074—5081, 2002. 

A second limiting physical/hydrodynamic case is the soil as a porous bed. Often others simulate undisturbed soils in the lab with soil columns, however we have chosen to use a slice of such a column a differential volume reactor (DVR)-as the experimental design (22). This approach offers advantages in the ability to develop a more spatially homogeneous system and also contributes to the perturbation/response analysis needed for systems identification. 

P. Eykhoff. 1974. "System Identification, Parameter and State Estimation". John Wiley, New York. 

H. H. Kagiwada. 1974. "System Identification, Methods and Applications". Addison-Westley, Reading. 

L. Ljung. 1987. "System Identification Theory for the User". Prentice-Hall, Englewood Clifis. 

Koshigoe, S., T. Komatsuzaki, and V. Yang. 1999. Active control of combustion instability with on-line system identification. J. Propulsion Power 15 383-89. 

---