Two-Step Process Identification With Correlation Analysis and Least-Squares Parameter Estimation

An identification method is described which first identifies a linear nonparametric model (crosscorrelation function, impulse response) by correlation analysis and then estimates the parameters of a parametric model (discrete transfer function) and also includes a method for the detection of the model order and the time delay. The performance, the computational expense and the overall reliability of this method is compared with five other identification methods. This two-step identification method, which can be applied off-line or on-line, is especially suited to identification by process computers, since it has the properties: Little a priori knowledge about the structure of the process model; very short computation time; small computer storage; no initial values of matrices and parameters are necessary and no divergence is possible for the on-line version. Results of an on-line identification of an industrial process with a process computer are shown.

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A competitive algorithm for managing sharing in the distributed execution of functional programs

Journal of Functional Programming ◽

10.1017/s095679689700275x ◽

1997 ◽

Vol 7 (4) ◽

pp. 421-440

Author(s):

GAD AHARONI ◽

AMNON BARAK ◽

AMIR RONEN

Keyword(s):

A Priori ◽

Computation Time ◽

Cost Effective ◽

Time Algorithm ◽

Communication Overhead ◽

Communication Costs ◽

Processing Elements ◽

On Line ◽

Distributed Execution ◽

Run Time

Execution of functional programs on distributed-memory multiprocessors gives rise to the problem of evaluating expressions that are shared between several Processing Elements (PEs). One of the main difficulties of solving this problem is that, for a given shared expression, it is not known in advance whether realizing the sharing is more cost effective than duplicating its evaluation. Realizing the sharing requires coordination between the sharing PEs to ensure that the shared expression is evaluated only once. This coordination involves relatively high communication costs, and is therefore only worthwhile when the shared expressions require much computation time to evaluate. In contrast, when the shared expression is not computation intensive, it is more cost effective to duplicate the evaluation, and thus avoid the communication overhead costs. This dilemma of deciding whether to duplicate the work or to realize the sharing stems from the unknown computation time that is required to evaluate a shared expression. This computation time is difficult to estimate due to unknown run-time evolution of loops and recursion that may be part of the expression. This paper presents an on-line (run-time) algorithm that decides which of the expressions that are shared between several PEs should be evaluated only once, and which expressions should be evaluated locally by each sharing PE. By applying competitive considerations, the algorithm manages to exploit sharing of computation-intensive expressions, while it duplicates the evaluation of expressions that require little time to compute. The algorithm accomplishes this goal even though it has no a priori knowledge of the amount of computation that is required to evaluate the shared expression. We show that this algorithm is competitive with a hypothetical optimal off-line algorithm, which does have such knowledge, and we prove that the algorithm is deadlock free. Furthermore, this algorithm does not require any programmer intervention, it has low overhead, and it is designed to run on a wide variety of distributed systems.

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An Adaptive Identification Method Based on the Modulating Functions Technique and Exact State Observers for Modeling and Simulation of a Nonlinear Miso Glass Melting Process

International Journal of Applied Mathematics and Computer Science ◽

10.2478/amcs-2019-0055 ◽

2019 ◽

Vol 29 (4) ◽

pp. 739-757 ◽

Cited By ~ 1

Author(s):

Witold Byrski ◽

Michał Drapała ◽

Jȩdrzej Byrski

Keyword(s):

Glass Melting ◽

Industrial Process ◽

Model Parameters ◽

Identification Algorithm ◽

Process Data ◽

Adaptive Identification ◽

Identification Method ◽

State Observers ◽

On Line ◽

Modulating Functions

Abstract The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation.

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On-Line Identification of Process Model Based on Swarm Intelligence Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3216 ◽

2011 ◽

Vol 403-408 ◽

pp. 3216-3219 ◽

Cited By ~ 2

Author(s):

Li Ting Cao ◽

Qi Bing Jin ◽

Tong Shun Fan ◽

Wei Su

Keyword(s):

Process Model ◽

Closed Loop ◽

Estimation Algorithm ◽

System Optimization ◽

Step Response ◽

Identification Methods ◽

Identification Method ◽

Line Identification ◽

On Line ◽

Intelligent Technology

On-line identification problem of process model was discussed in this paper, which use warm intelligent technology. An on-line identification method based on HPSO-Rosenbrock parameter estimation algorithm is proposed to solve the problem that traditional identification methods cannot be used in continuous-time systems on closed-loop step response conditions. This identification method is a combined method of a modified PSO and Rosenbrock which can make full use of global search ability of PSO and local search ability of Rosenbrock. Identification results of HPSO-Rosenbrock algorithm were made and compared with the other identification methods. The simulation and compare results show that the on-line identification method proposed in this paper is an approximate unbiased and effective identification method. This method can be successfully applied to closed-loop identification under secious noise and big dead-time object which provides a new idea for system optimization and advanced control.

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Image registration with a computer driven S.T.E.M.

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100107630 ◽

1978 ◽

Vol 36 (1) ◽

pp. 98-99

Author(s):

A.M.H. Schepman ◽

J.A.P. van der Voort ◽

J.E. Mellema

Keyword(s):

Spot Size ◽

Scanning Transmission Electron Microscope ◽

Small Computer ◽

Structural Studies ◽

Area Of Interest ◽

Transmission Electron ◽

Scanning Transmission ◽

Specimen Area ◽

On Line ◽

Minimum Distances

A Scanning Transmission Electron Microscope (STEM) was coupled to a small computer. The system (see Fig. 1) has been built using a Philips EM400, equipped with a scanning attachment and a DEC PDP11/34 computer with 34K memory. The gun (Fig. 2) consists of a continuously renewed tip of radius 0.2 to 0.4 μm of a tungsten wire heated just below its melting point by a focussed laser beam (1). On-line operation procedures were developped aiming at the reduction of the amount of radiation of the specimen area of interest, while selecting the various imaging parameters and upon registration of the information content. Whereas the theoretical limiting spot size is 0.75 nm (2), routine resolution checks showed minimum distances in the order 1.2 to 1.5 nm between corresponding intensity maxima in successive scans. This value is sufficient for structural studies of regular biological material to test the performance of STEM over high resolution CTEM.

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