Computer systems, identification method

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. 

If the mappings T in the sensor equation (6.75) and the actuator equation (6.76) are purely hysteretic they can be modeled by a Prandtl-Ishhnskii operator H, a modified Prandtl-Ishlinskii operator M or a Preisach hysteresis operator R depending on the degree of symmetry of the branching behaviour. The calculation of these hysteresis operators and the corresponding compensators from the measured output-input characteristic requires special computer-aided synthesis procedures which is based on system identification methods. Due to a lack of space, this article cannot further comment on these synthesis methods. However, a detailed description of both the synthesis method and the mathematical basics can be found in the literature [332,341,350-352,356]. 

Reynders E (2012) System identification methods for (operational) modal analysis review and comparison. Arch Comput Method Eng 19(1) 51-124... 

Decomposition leads to a rearrangement of the process equations from their flow chart sequence to a natural sequence based on the information flow among the equations. The ultimate goal is to set up an iterative scheme in which each equation is solved for a single variable (by some appropriate root identification method), and where values of unknown variables that must be assumed are checked cyclically. The greatest reduction in the number of iterates that must be assumed, and therefore the greatest reduction in computer storage and time requirements, takes place for those systems of process equations in which the number of variables per equation is small compared to the total number of variables in the system. Clearly, when each of the system equations contains every process variable, no effective decomposition can take place. Fortunately, most models used in the process industries are of such a character that extensive decomposition can be effected. 

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,... 

The established methodology for computer system validation enables identification and control of each life-cycle phase and its associated document deliverables. It is also recognized that throughout the validation life cycle there is a level of dependency on the methods, services, and resources of the computer system supplier. 

The purpose of the following checklist is to help to determine if a computer system complies with the FDA Rule 21 CFR 21 Part 11 for electronic records and electronic signatures. This audit questionnaire apphes to systems that meet the definition of a closed system as defined in Section 11.3 (b)(4) of the rule and which do not utilize biometrics identification methods. 

Mass spectrometry (MS) is now a well-accepted tool for the identification as well as quantitation of unknown compounds. The combination of MS with powerful separation methods such as gas chromatography (GC) or high-performance liquid chromatography (LC) provides a technique which is widely accepted for the identification of unknown components in complex mixtures from a wide variety of problems such as environmental pollutants, biological fluids, insect pheromones, chemotaxonomy, and synthetic fuels. The importance of such analyses has grown exponentially in the last few years there are now well over a thousand GC/MS instruments in use around the world, most with dedicated computer systems which make possible the collection from each of hundreds of unknown mass spectra per day (1). 

In many of the early applications of fuzzy logic, the s and B s in the if-then rules had to be calibrated by cut-and-trial to achieve a desired level of performance. During the past few years, however, the techniques related to the induction of rules from observations have been developed to a point where the calibration of rules—by induction from input-output pairs—can be automated in a wide variety of cases. Particularly effective in this regard are techniques centered on the use of neural network methods and genetic computing for purposes of system identification and optimization. Many of the so-called neuro-fuzzy and fuzzy-genetic systems are of this type. 

The variations in human metabolic profiles can seldom permit visual observations of meaningful metabolic deviations from the normal. However, large computer systems do have the general capability to extract the distinct features from large data sets, and reduce the bulk of data from capillary GC of numerous patients to a more easily understandable form. Precisely measured retention characteristics and the peak areas form the basis for such comparisons. Pattern recognition methods have been utilized to classify diabetic samples [169,170] and those of virus-infected patients [171] with the aid of training sets from clinically defined cases. In addition, the feature extraction approach [169,170] permits identification of important metabolite peaks in complex chromatograms. 

Chapter 13 surveys methods of system identification in physiology, the process of extracting models or model components from experimental data. Identification typically refers to model specification or model estimation, where unknown parameters are estimated within the specified model using experimental data and advanced computational techniques. Estimation may be either parametric, where algebraic or difference equations represent static or dynamic systems, or nonparametric, where analytical (convolution), computational (look-up tables), or graphical (phase-space) techniques characterize the system. This chapter closes with a recent hybrid modular approach. 

The most advanced and sophisticated methods for impact identification rely on expert systems (Rodriguez-Bachiller and Glasson, 2003), defined as computer systems that emulate the decision-making abUity of a human expert (Jackson, 1998). The basic idea behind expert systems is that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer and then stored in the computer and users call upon the computer for specific advice as needed (Liao, 2005). Several expert systems have been proposed in the hterature (Liu and Lai, 2009). Among them, two categories are noteworthy. The use of analytic hierarchy... 

New methods for threat identification that can be executed with up-to-date IT methods and new measuring and diagnostic technologies must be the main point of interest in education. Herein we can use various methods of 3-D simulation, for example, in the form of virtual reality. These tools help designers and developers to execute threat analyses in the design stage, on a computer screen. These methods help to shape machine operation, as well as complex mechanical systems by executing analyses to determine the activities in which to expect threats and the risks associated with them. Scientists emphasize the development of new methods as opposed... 

Beck J (2009) Using model classes in system identification for robust response predictions. In Proceedings of COMPDYN 2009, ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Rhodos... 

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