Search results for "System identification"
showing 10 items of 56 documents
An output-only stochastic parametric approach for the identification of linear and nonlinear structures under random base excitations: Advances and c…
2014
In this paper a time domain output-only Dynamic Identification approach for Civil Structures (DICS) first formulated some years ago is reviewed and presented in a more generalized form. The approach in question, suitable for multi- and single-degrees-of-freedom systems, is based on the statistical moments and on the correlation functions of the response to base random excitations. The solving equations are obtained by applying the Itô differential stochastic calculus to some functions of the response. In the previous version ([21] Cavaleri, 2006; [22] Benfratello et al., 2009), the DICS method was based on the use of two classes of models (Restricted Potential Models and Linear Mass Proport…
RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
2021
The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…
State Space-Vector Model of Linear Induction Motors Including End-effects and Iron Losses - Part II: Model Identification and Results
2020
This is the second part of an article, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost …
Experimental System Identification and Black Box Modeling of Hydraulic Directional Control Valve
2015
Directional control valves play a large role in most hydraulic systems. When modeling the hydraulic systems, it is important that both the steady state and dynamic characteristics of the valves are modeled correctly to reproduce the dynamic characteristics of the entire system. In this paper, a proportional valve (Brevini HPV 41) is investigated to identify its dynamic and steady state characteristics. The steady state characteristics are identified by experimental flow curves. The dynamics are determined through frequency response analysis and identified using several transfer functions. The paper also presents a simulation model of the valve describing both steady state and dynamic charac…
Parameter identification of induction motor model using genetic algorithms
1998
The paper deals with methods of identification of the parameters of an induction motor model using genetic algorithms. It is supposed that the inverter supplying the motor is directly accessible for control of the conduction sequences of its power switches. This makes it possible to carry out a test consisting of a transient from standstill to steady-state operation at a given frequency and successive free motion to standstill. During this test, data are acquired referring to stator voltages, and currents and speed. Then, a genetic algorithm is employed with the aim of determining the mechanical and electrical parameters of the model, so as to reproduce the input-output behaviour of a real …
NARX Models of an Industrial Power Plant Gas Turbine
2005
This brief reports the experience with the identification of a nonlinear autoregressive with exogenous inputs (NARX) model for the PGT10B1 power plant gas turbine manufactured by General Electric-Nuovo Pignone. Two operating conditions of the turbine are considered: isolated mode and nonisolated mode. The NARX model parameters are estimated iteratively with a Gram-Schmidt procedure, exploiting both forward and stepwise regression. Many indexes have been evaluated and compared in order to perform subset selection in the functional basis set and determine the structure of the nonlinear model. Various input signals (from narrow to broadband) for identification and validation have been consider…
Electronic noses: a review of signal processing techniques
1999
The field of electronic noses, electronic instruments capable of mimicking the human olfactory system, has developed rapidly in the past ten years. There are now at least 25 research groups working in this area and more than ten companies have developed commercial instruments, which are mainly employed in the food and cosmetics industries. Most of the work published to date, and commercial applications, relate to the use of well established static pattern analysis techniques, such as principal components analysis, discriminant function analysis, cluster analysis and multilayer perceptron based neural networks. The authors first review static techniques that have been applied to the steady-s…
An efficient diagnostic technique for distribution systems based on under fault voltages and currents
2010
Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental co…
Identification and robust control of DC/DC converter Hammerstein model
2008
Abstract—This paper deals with model-based robust control of dc/dc power electronic converters. The converter is described by means of a Hammerstein model consisting of the nonlinear static characteristics of the converter and a linear time-invariant (LTI) uncertainmodel whose parameters depend on the actual duty-cycle operating range. This suggests that the controller be designed using robust control techniques. In view of applying robust control, identification of the earlier LTI models is performed by means of simulation experiments, carried out on a converter switching model implemented onMATLAB/SIMULINK environment. Internal model control (IMC) structure is employed for the controller …