Showing papers on "System identification published in 1997"

On the "Identification and control of dynamical systems using neural networks"

Abstract: Referring to the above said paper by Narendra-Parthasarathy (ibid., vol.1, p4-27 (1990)), it is noted that the given Example 2 (p.15) has a third equilibrium state corresponding to the point (0.5, 0.5).

Using wavelet network in nonparametric estimation

Abstract: Wavelet networks are a class of neural networks consisting of wavelets. In this paper, algorithms for wavelet network construction are proposed for the purpose of nonparametric regression estimation. Particular attentions are paid to sparse training data so that problems of large dimension can be better handled. A numerical example on nonlinear system identification is presented for illustration.

Blind system identification

Abstract: Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input.

Prediction error method for second-order blind identification

Abstract: Blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest. In this paper, we introduce a second-order blind identification technique based on a linear prediction approach. In contrast to eigenstructure-based methods, it will be shown that the linear prediction error method is "robust" to order overdetermination. An asymptotic performance analysis of the proposed estimation method is carried out, consistency and asymptotic normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are finally presented to demonstrate the potential and the "robustness" of the proposed method.

CNC Machine Accuracy Enhancement Through Real-Time Error Compensation

Abstract: Improving CNC machine tool accuracy has received significant attention recently. This paper intends to provide an introduction of the real-time error compensation methods as applied to reduce both geometric and thermally induced quasistatic machine tool errors. An illustrative example is used to demonstrate the use of error compensation systems for a horizontal machining center. Although several industrial applications of these error compensation systems have achieved significant results, a few major barriers have prevented this promising technology from being applied widely in manufacturing. Several ongoing research activities aimed at overcoming the barriers are also presented.

Frequency domain system identification using arbitrary signals

Abstract: It is the common conviction that frequency domain system identification suffers from the drawback that it cannot handle arbitrary signals without introducing systematic errors. This paper shows that it is possible to deal with nonperiodic signals without any approximation and under the same assumptions as in the time domain, by estimating simultaneously some initial conditions and the system model parameters.

Fuzzy model identification: selected approaches

Abstract: General Overview.- Fuzzy Identification from a Grey Box Modeling Point of View.- 1. Introduction.- 2. System Identification.- 3. Fuzzy Modeling Framework.- 4. Fuzzy Identification Based on Prior Knowledge.- 5. Example - Tank Level Modeling.- 6. Practical Aspects.- 7. Conclusions and Future Work.- References.- Clustering Methods.- Constructing Fuzzy Models by Product Space Clustering.- 1. Introduction.- 2. Overview of Fuzzy Models.- 3. Structure Selection for Modeling of Dynamic Systems.- 4. Fuzzy Clustering.- 5. Deriving Takagi-Sugeno Fuzzy Models.- 6. Example: pH Neutralization.- 7. Practical Considerations and Concluding Remarks.- A. The Gustafson-Kessel Algorithm - MATLAB Implementation.- References.- Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform.- 1. Introduction.- 2. The Identification Method.- 3. Example 1.- 4. Example 2.- 5. Summary of the Identification Procedure.- 6. Practical Considerations and Concluding Remarks.- References.- Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering.- 1. Introduction.- 2. The Fuzzy C-Means Algorithm.- 3. Using Hierarchical Clustering to Preprocess Data.- 4. Rapid Prototyping of Approximative Fuzzy Models.- 5. Rapid Prototyping of Descriptive Fuzzy Models.- 6. Examples.- 7. Practical Considerations and Concluding Remarks.- A. Proofs of Propositions.- References.- Neural Networks.- Fuzzy Identification Using Methods of Intelligent Data Analysis.- 1. Introduction.- 2. Neuro-Fuzzy Methods.- 3. Density Estimation.- 4. Fuzzy Clustering.- 5. Conclusion.- A. From Rules to Networks.- B. Learning Rule for RBF Networks.- C.Update Equations for Gaussian Mixtures.- D. Adaptation Algorithm for Fuzzy Clustering.- References.- Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN.- 1. Introduction.- 2. Classification of Fuzzy Models.- 3. Fuzzy Neural Networks.- 4. Identification of Singleton Fuzzy Models.- 5. Simulation Results.- 6. Practical Considerations and Concluding Remarks.- References.- Genetic Algorithms.- Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms.- 1. Introduction.- 2. Evolutionary Algorithms and Genetic Fuzzy Systems.- 3. The Fuzzy Model Identification Problem.- 4. The Genetic Fuzzy Identification Method.- 5. Example.- 6. Practical Considerations and Concluding Remarks.- References.- Optimization of Fuzzy Models by Global Numeric Optimization.- 1. Introduction.- 2. Theoretical Aspects of Fuzzy Models.- 3. The Fuzzy Identification Method.- 4. Simulation Results.- 5. Practical Aspects.- References.- Artificial Intelligence.- Identification of Linguistic Fuzzy Models Based on Learning.- 1. Introduction.- 2. Basic Concepts and Notation.- 3. The Identification Problem.- 4. The Fuzzy Identification Method.- 5. Numeric Examples.- 6. Practical Aspects and Concluding Remarks.- References.

Matrix outer-product decomposition method for blind multiple channel identification

Abstract: Blind channel identification and equalization have attracted a great deal of attention due to their potential application in mobile communications and digital TV systems. In this paper, we present a new algorithm that utilizes second-order statistics for multichannel parameter estimation. The algorithm is simple and relies on an outer-product decomposition. Its implementation requires no adjustment for single- or multiple-user systems. This new algorithm can be viewed as a generalization of a linear prediction algorithm. It is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimates. The superior performance of this new algorithm is demonstrated through simulation examples.

Modeling and simulation of induction machine vector control with rotor resistance identification

Abstract: This paper shows that it is possible to use available commercial software to model and simulate a vector-controlled induction machine system. The components of a typical vector control system are introduced and methods given for incorporating these in the MATLAB/SIMULINK software package. The identification of rotor resistance is important in vector control, if high-performance torque control is needed, and modeling of the extended Kalman filter (EKF) algorithm for parameter identification is discussed. It is certainly advisable, when feasible, to precede implementation of new algorithms, whether for control or identification purposes, with an extensive simulation phase. Additionally, a technique for generating pulse-width modulation (PWM) phase commands to extend machine operation to higher speeds before field weakening occurs is simulated in a vector-controlled induction machine, driven by a PWM inverter. This demonstrates the versatility of the vector-controlled induction machine system model.

Nonparametric approach to Wiener system identification

Abstract: A Wiener system, i.e., a system consisting of a linear dynamic subsystem followed by a memoryless nonlinear one is identified. The system is driven by a stationary white Gaussian stochastic process and is disturbed by Gaussian noise. The characteristic of the nonlinear part can be of any form. The dynamic subsystem is asymptotically stable. The a priori information about both the impulse response of the dynamic part of the system and the nonlinear characteristics is nonparametric. Both subsystems are identified from observations taken at the input and output of the whole system. The kernel regression estimate is applied to estimate the invertible part of the nonlinearity. An estimate to recover the impulse response of the dynamic part is also given. Pointwise consistency of the first and consistency of the other estimate is shown. The results hold for any nonlinear characteristic, and any asymptotically dynamic subsystem. Convergence rates are also given.

A delay damage model selection algorithm for NARX neural networks

Abstract: Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intelligent memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction.

System Identification with Limited Observations and without Input

Abstract: A time domain system identification technique is proposed to estimate the stiffness and damping parameters, at the element level, of a structure excited by unknown or unmeasured input forces. The unknown input forces could be of any type, including seismic loading. The unique feature of this technique is that it does not require response measurements at all dynamic degrees of freedom of the structure. This new procedure is a combination of an iterative least-squares procedure with unknown input excitations (ILS-UI) proposed earlier by the writers, and the extended Kalman filter method with a weighted global iteration (KF-WGI). The new procedure is denoted by the writers as ILS-EKF-UI. The uncertainty in the output responses is considered, and its effect on the accuracy of the identified parameters is analyzed. The efficiency, accuracy, and robustness of the proposed algorithm are illustrated by numerical examples. The accuracy of the proposed ILS-EKF-IU procedure is of the same order as that of ILS-UI; ho...

Identification and modelling of a three phase arc furnace for voltage disturbance simulation

Abstract: This paper presents a new arc furnace model which copes with the two main voltage disturbances normally associated with arc furnaces: voltage fluctuations; and harmonics. The model is based on the stochastic nature of the electric arc current-voltage characteristic. The model has been estimated from measurements made in two actual electric plants. Although a single-phase model has been normally proposed, this paper develops a three-phase model in order to fully represent the unbalances that are present in real industrial plants and which play a central role in the behavior of compensation devices such as SVCs. The model has been implemented using the SIMULINK environment in order to facilitate later simulation of advanced disturbance control systems. Finally, the simulation results are compared with actual data in order to validate the accuracy of the model.

Method for developing a neural network tool for process identification

Abstract: A tool, and the method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The tool and method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trained using a process model to approximate a function which relates process input and output data to process parameter values. Once trained, the network can be used as a system identification tool. In principle, this approach can be used for linear or nonlinear processes, for open or closed loop identification, and for identifying any or all process parameters.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Abstract: Addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, the authors investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, the authors show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. They compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, the authors show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Dynamic non-Singleton fuzzy logic systems for nonlinear modeling

Abstract: We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS's can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process.

Experimental Evaluation of Identification Schemes on a Direct Drive Robot

Abstract: This paper describes the experimental evaluation of three identification schemes to determine the dynamic parameters of a two degrees of freedom direct-drive robot. These schemes involve a recursive estimator while the regression models are formulated in continuous time. The fact that the total energy of robot manipulators can be represented as a linear relation in the inertial parameters, has motivated the suggestion in the literature of several regression models which are linear in a common dynamic parameter vector. Among them, in this paper we consider the schemes based on the filtered dynamic regression model, the supplied energy regression model and a new one proposed in this paper: the filtered power regression model. The underling recursive parameter estimator used in the experimental evaluation is the standard least-squares.

Suboptimal feedback control by a scheme of iterative identification and control design

Abstract: In this paper a framework for an iterative procedure of identification and robust control design is introduced wherein the robust performance is monitored during the subsequent steps of the iterative scheme. By monitoring the performance via a model-based approach, the possibility to guarantee performance improvement in the iterative scheme is being employed. In order to monitor achieved performance (for a present controller) and to guarantee robust performance (for a future controller), an uncertainty set is used where the uncertainty structure is chosen in terms of model perturbations in the dual Youla parametrization. This uncertainty structure is shown to be particularly suitable for the control performance measure that is considered. The model uncertainty set can be identified by an uncertainty estimation procedure on the basis of closed-loop experimental data. To obtain performance robustness, robust control design tools are used to synthesise controllers on the basis of the identified uncertainty set.

Algorithms for minimal model structure detection in nonlinear dynamic system identification

Abstract: The minimal model structure detection (MMSD ) problem in nonlinear dynamic system identification is formulated as a search for the optimal orthogonalization path. While an exhaustive search for a model with 20 candidate terms would involve 2.43 1018 possible paths, it is shown that this can typically be reduced to 2 103 by augmenting the orthogonal estimation algorithm with genetic search procedures. The MMSD algorithm provides the first practical solution for optimal structure detection in NARMAX modelling, training neural networks and fuzzy systems modelling. Based on the MMSD algorithm, a refined forward regression orthogonal (RFRO ) algorithm is developed. The RFRO algorithm initially detects a parsimonious model structure using the forward regression orthogonal algorithm and then refines the model structure by applying the MMSD algorithm to the reduced model term set. The RFRO algorithm cannot guarantee to find the minimal model structure, but it is computationally more efficient than the MMSD algori...