Finite Impulse Response Model Research Articles

Optimization-Oriented High Fidelity NFIR Models for Estimating Indicated Torque in Diesel Engines

In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively.

Robust Parameter Estimation for a Class of Nonlinear System With EM Algorithm

This paper is concerned with the robust parameter estimation for linear parameter varying (LPV) finite impulse response (FIR) model. The practical process data are typically polluted by outliers and conventional parameter estimation methods may fail to derive an unbiased estimate. In order to deal with outliers, the Laplace noise model is adopted and the robust system model for the described parameter estimation problem is established. The robust parameter estimation for LPV FIR model is formulated and solved in the EM algorithm scheme and the equations to estimate all the unknown parameters are derived. The efficacy of the proposed method is verified through a numerical simulation and a chemical unit.

An Adaptive Algorithm for Active Vibration Control of Parameter-Varying Systems With a New Online Secondary Path Estimation Method

An adaptive algorithm is proposed on the basis of the filtered-x least mean square (FxLMS) algorithm for active control of parameter-varying vibration systems. The adaptive algorithm utilizes an auxiliary filter to improve online estimation of the secondary path. The secondary path is represented by the transfer functions of the auxiliary filter and the controller, and its finite impulse response (FIR) model is estimated with the LMS algorithm. Convergence analysis of the adaptive algorithm is presented as well. Both simulation and experiment were conducted to evaluate the effectiveness of the adaptive algorithm. The results have demonstrated that the adaptive algorithm is effective even in case of an abrupt change in the parameters of the secondary path.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease.

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

Memory Pattern Identification for Feedback Tracking Control in Human-Machine Systems.

The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor. Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a one-dimensional tracking task. Specifically, data recorded from human subjects controlling dynamic systems with different fractional order were investigated. A finite impulse response (FIR) controller was fitted to the data, and pattern analysis of the fitted parameters was performed. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closed-loop was conducted. It is shown that the FIR model can be used to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less than or equal to 1. For systems of different fractional order, the proposed control scheme-based on an FIR model-can effectively characterize the linear properties of manual control in humans. This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are the following: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

Ensemble regularized local finite impulse response models and soft sensor application in nonlinear dynamic industrial processes

This paper proposes a novel adaptive soft sensor modeling method based on ensemble of regularized local finite impulse response (FIR) models, which are estimated by using the stable kernel-based regularized least squares (SKRLS), for quality prediction of nonlinear dynamic industrial processes, referred to as the ELFIR-SKRLS. Meanwhile, the proposed ELFIR-SKRLS approach provides an effective way to extend the FIR-SKRLS for nonlinear dynamic system under the ensemble learning framework, and the tricky question of model order selection can be avoided. To deal with the process nonlinearity, an improved adaptive local domain partition method is employed to adaptively partition the historical data set into multiple local domains, where the optimal benchmark window selection mechanism is proposed to ensure the samples within the benchmark window belong to the same process state. In addition, a novel prediction performance-based adaptive ensemble method is proposed to combine the outputs of local FIR models, which is based on the weighted Euclidean distance-based similarity criterion and a strategy of reducing the online computation burden. The effectiveness of the proposed ELFIR-SKRLS adaptive soft sensor is demonstrated through a sulfur recover unit process application and a ladle furnace steel refining process application.

Comprehensive Optimal Fir Filter Design Procedures With Various Impacts

Digital-signal-processing (DSP) is one of the recent emerging techniques contain more filtering operations. It may an image type or audio/ video signal processing. Each processing unit has filtering sections to filter noise elements. Hence, there is a need for efficient and secure algorithmic scheme. Here, a exhaustive scrutiny use of complex optimization algorithms towards the digital-filter construction is conferred. In appropriate, the scrutiny target on the identification of various suggestions and limitations in FIR system design. For exact representations, the infinite impulse response adaptive filters and finite impulse response models are considered for estimation. It is designed to review a various swarm and evolutionary computing structures employed for filter design schemes. Some popular computing algorithms are noticed to recover characteristics of percolate design approach. Further, compared with recent research for identifying the updating features in optimization schemes. Finally, this review suggested that the swarm intelligence based researchers improved the constraints and its attributes.

Hemodynamic Response Function Modeling to Determine the Areas with High Blood Supply in Block-Design fMRI Experiments

Background: Determining the activated brain areas due to different activities is one of the most common targets in functional magnetic resonance imaging (fMRI) data analysis, which could be carried out by hemodynamic response function (HRF) evaluation. The HR functions reflect changes of cerebral blood flow (CBF) in response to neural activity. Objectives: In this study, five models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data. Methods: The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO2 gas was administered using a face-mask under physiological monitoring. Three patients with brain tumors were scanned. The fMRI data analysis was performed using the SPM 12 MATLAB toolbox. Akaike’s information criterion (AIC), Schwarz’ Bayesian (SBC), and mean square error (MSE) criteria were used to select the best HRF estimation model. Results: In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), finite impulse response (FIR), and inverse logistic (IL) models were almost equal to the standard HRF. Mean square error, AIC, and SBC indices were ignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies. Conclusions: The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood flow.

Distinct temporal brain dynamics in bipolar disorder and schizophrenia during emotion regulation.

Disturbances in emotion regulation (ER) are characteristic of both patients with bipolar disorder (BD) and schizophrenia (SZ). We investigated the temporal dynamics of brain activation during cognitive ER in BD and SZ to understand the contribution of temporal characteristics of disturbed ER to their unique and shared symptomatology. Forty-six participants performed an ER-task (BD, n = 15; SZ, n = 16; controls, n = 15) during functional magnetic resonance imaging, in which they were instructed to use cognitive reappraisal techniques to regulate their emotional responses. Finite impulse response modeling was applied to estimate the temporal dynamics of brain responses during cognitive reappraisal (v. passive attending) of negative pictures. Group, time, and group × time effects were tested using multivariate modeling. We observed a group × time interaction during ER in the ventrolateral prefrontal cortex (VLPFC), supplementary motor area (SMA) and inferior occipital gyrus. Patients with SZ demonstrated initial hyper-activation of the VLPFC and SMA activation that was not sustained in later regulatory phases. Response profiles in the inferior occipital gyrus in SZ showed abnormal activation in the later phases of regulation. BD-patients showed general blunted responsivity in these regions. These results suggest that ER-disturbances in SZ are characterized by an inefficient initialization and failure to sustain regulatory control, whereas in BD, a failure to recruit regulatory resources may represent initial deficits in formulating adequate representations of the regulatory needs. This may help to further understand how ER-disturbances give rise to symptomatology of BD and SZ.

Quantification and Propagation of Uncertainties in Identification of Flame Impulse Response for Thermoacoustic Stability Analysis

The thermoacoustic behavior of a combustion system can be determined numerically via acoustic tools such as Helmholtz solvers or network models coupled with a model for the flame dynamic response. Within such a framework, the flame response to flow perturbations can be described by a finite impulse response (FIR) model, which can be derived from large eddy simulation (LES) time series via system identification. However, the estimated FIR model will inevitably contain uncertainties due to, e.g., the statistical nature of the identification process, low signal-to-noise ratio, or finite length of time series. Thus, a necessary step toward reliable thermoacoustic stability analysis is to quantify the impact of uncertainties in FIR model on the growth rate of thermoacoustic modes. There are two practical considerations involved in this topic. First, how to efficiently propagate uncertainties from the FIR model to the modal growth rate of the system, considering it is a high dimensional uncertainty quantification (UQ) problem? Second, since longer computational fluid dynamics (CFD) simulation time generally leads to less uncertain FIR model identification, how to determine the length of the CFD simulation required to obtain satisfactory confidence? To address the two issues, a dimensional reduction UQ methodology called “Active subspace approach (ASA)” is employed in the present study. For the first question, ASA is applied to exploit a low-dimensional approximation of the original system, which allows accelerated UQ analysis. Good agreement with Monte Carlo analysis demonstrates the accuracy of the method. For the second question, a procedure based on ASA is proposed, which can serve as an indicator for terminating CFD simulation. The effectiveness of the procedure is verified in the paper.

Gray-box identification with regularized FIR models

Abstract The problem of modeling a linear dynamic system is discussed and a novel approach to automatically combine black-box and white-box models is introduced. The solution proposed in this contribution is based on the usage of regularized finite-impulse-response (FIR) models. In contrast to classical gray-box modelling, which often only optimizes the parameters of a given model structure, our approach is able to handle the problem of undermodeling as well. Therefore, the amount of trust in the white-box or gray-box model is optimized based on a generalized cross-validation criterion. The feasibility of the approach is demonstrated with a pendulum example. It is furthermore investigated, which level of prior knowledge is best suited for the identification of the process.

Control valve stiction compensation by dynamic inversion: a comparative study

PurposeThe purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort.Design/methodology/approachThis paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques.FindingsThe new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness.Research limitations/implicationsThe compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered.Originality/valueThe presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.

A robust global approach for LPV FIR model identification with time-varying time delays

Robust identification of the linear parameter varying (LPV) finite impulse response (FIR) model with time-varying time delays is considered in this paper. A robust observation model based on Laplace distribution is established to deal with the output data contaminated with the outliers, which are commonly existed in modern industries. A Markov chain model is utilized to model the correlation between the time delays as they do not simply change randomly in reality. A transition probability matrix and an initial probability distribution vector are used to govern the switching mechanism of the time delays. Since it is difficult to optimize the complex log likelihood function directly, the derivations of the proposed algorithm are performed under the framework of Expectation-Maximization (EM) algorithm. A numerical example and a chemical process are utilized to verify the effectiveness of the proposed approach.

Evaluating the impact of uncertainty in flame impulse response model on thermoacoustic instability prediction: A dimensionality reduction approach

The flame response to upstream velocity perturbations is properly described by a Finite Impulse Response (FIR) model. When combining an FIR model with acoustic tools to predict thermoacoustic modal growth rates, uncertainties contained in the FIR model coefficients would propagate through the acoustic model, inducing deviations of the modal growth rate from its nominal value. Therefore, an associated uncertainty quantification (UQ) analysis, which focuses on quantifying the impact of FIR model uncertainties on the modal growth rate prediction, is a necessity to obtain a more reliable thermoacoustic instability prediction. To address this UQ problem, our present work proposes an analytical strategy featuring (1) compactly summarizing the causal relationship between variations of FIR model coefficients and variations of modal growth rates; (2) Effectively shrinking the dimension of the UQ problem; (3) Requiring only negligible computational cost; (4) Involving no complex mathematical treatments. Our case studies yielded 5000 times faster yet highly accurate UQ analyses compared with reference Monte Carlo simulations, even though a significant level of FIR model uncertainty is present. The analytical approach brings additional benefits including (1) visualization of the process from the variations of FIR model coefficients to the variations of modal growth rate; (2) Easily-obtainable sensitivity measurement for each FIR model coefficient, which can help identify key mechanisms controlling the thermoacoustic instability; (3) New possibility for robust combustor design, i.e., to minimize the impact of FIR model uncertainty on the thermoacoustic instability prediction.

Systems Analysis of Human Visuo-Myoelectric Control Facilitated by Anodal Transcranial Direct Current Stimulation in Healthy Humans.

Induction of neuroplasticity by transcranial direct current stimulation (tDCS) applied to the primary motor cortex facilitates motor learning of the upper extremities in healthy humans. The impact of tDCS on lower limb functions has not been studied extensively so far. In this study, we applied a system identification approach to investigate the impact of anodal transcranial direct current stimulation of the leg area of the motor cortex via the human visuo-myoelectric controller. The visuo-myoelectric reaching task (VMT) involves ballistic muscle contraction after a visual cue. We applied a black box approach using a linear ARX (Auto-regressive with eXogenous input) model for a visuomotor myoelectric reaching task. We found that a 20th order finite impulse response (FIR) model captured the TARGET (single input)—CURSOR (single output) dynamics during a VMT. The 20th order FIR model was investigated based on gain/phase margin analysis, which showed a significant (p < 0.01) effect of anodal tDCS on the gain margin of the VMT system. Also, response latency and the corticomuscular coherence (CMC) time delay were affected (p < 0.05) by anodal tDCS when compared to sham tDCS. Furthermore, gray box simulation results from a Simplified Spinal-Like Controller (SSLC) model demonstrated that the input-output function for motor evoked potentials (MEP) played an essential role in increasing muscle activation levels and response time improvement post-tDCS when compared to pre-tDCS baseline performance. This computational approach can be used to simulate the behavior of the neuromuscular controller during VMT to elucidate the effects of adjuvant treatment with tDCS.

YAPAY ARI KOLONİSİ ALGORİTMASI İLE OPTİMİZE EDİLEN HAMMERSTEIN MODEL KULLANARAK SİSTEMLERİN KİMLİKLENDİRİLMESİ

Hammerstein model is formed by cascade of linear and nonlinear parts. In literature, memoryless polynomial nonlinear (MPN) model for nonlinear part and finite impulse response (FIR) model or infinite impulse response (IIR) model for linear part are mostly preferred for Hammerstein models. This paper different from the studies in literature, focuses on the success of Hammerstein block model that Second Order Volterra (SOV) is preferred instead of MPN as nonlinear part. In this context , a new Hammerstein model is presented which is obtained by cascade form of a nonlinear SOV and a linear FIR model. In simulations, different types of system are identified by proposed Hammerstein model which is optimized with ABC (artificial bee colony) algorithm. The simulation results reveal effectiveness and robustness of the proposed model with ABC algorithm.

Kalman filter-based centralized controller design for non-square multi-input multi-output processes

In this paper, a simple and effective centralized controller is presented to control non-square multi-input multi-output industrial processes with heavy interactions and significant time-delays. The control objective is to achieve a good load disturbance rejection and set point tracking. The controller and process model equations are represented in a stochastic state-space. Also, the state and measurement equations respectively use general random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function including the control error vector as well as the input vector and its changes is introduced. A Kalman filter algorithm is used to solve the discrete algebraic Riccati equation and to estimate the state vector of the controller recursively. This novel use of Kalman filter combines the observer and controller in a block. No square down/up, pairing and pseudo-inversing, is required for the proposed control structure. This leads to the simplicity of controller design. To evaluate the performance of the proposed control strategy, it is applied to a simulation example and an industrial case study with heavy interactions and significant time delays, an atmospheric distillation column with 7 inputs and 4 outputs. Simulation results show that the proposed controller has a good performance in both load disturbance rejection and setpoint tracking.

Identification of FIR Models for LTI Multiscale Systems using Sparse Optimization Techniques

Finite impulse response (FIR) models are very popular in process industries because of their simple model structure, flexibility to explain arbitrary complex stable linear dynamics and finally their ease of implementation in on-line applications. In general, identification of FIR models requires large number of parameters to be estimated. In case of systems with multiple time scales, the length of FIR model structure under conventional uniform sampling becomes arbitrarily high due to simultaneous presence of fast and slow dynamics. This results in more variability in the estimated parameters when the conventional methods such as ordinary least squares are used. In this work, the FIR model estimation problem is formulated as a sparse optimization problem, where the sparse representation of impulse response coefficients for linear-time invariant multiscale systems in the time-frequency domain is exploited in order to explain the overall FIR model effectively with fewer number of coefficients and thereby incurring less variability in the estimated parameters. The effectiveness of proposed methodology is demonstrated by means of simulation case studies.

Positive FIR System Identification using Maximum Entropy Prior

Bayesian nonparametric methods have been introduced in a linear system identification paradigm to avoid the model and order selection problem. In this framework, a finite impulse response (FIR) model is considered, and the impulse response is realized as a zero-mean Gaussian process. The identification results mainly depend on a prior covariance (kernel) which has to be estimated from data. But the Gaussian prior assumption is inappropriate when the impulse response is constrained on an interval. This paper considers the positive FIR model identification problem using non Gaussian prior where a positive model denotes a system with the nonnegative impulse response. A suitable prior is selected as the maximum entropy prior when the impulse response has interval constraints. A truncated multivariate normal prior is shown to be the maximal entropy prior for positive FIR model identification. Simulation results demonstrate that the proposed prior shows significantly better robustness.

The Effect of Prior Knowledge on the Least Costly Identification Experiment

Design of optimal input excitations is one of the most challenging problems in the field of system identification. The main difficulty lies in the fact that the optimization problem cannot always be formulated to be convex, therefore a globally optimal excitation for the dynamic system of interest cannot be guaranteed. In this paper, optimal input design (OID) for linear systems in the presence of prior knowledge is studied. Information related to exponential decay and smoothness is incorporated in the optimal input design problem by making use of the Bayes rule of information. Three different cases of modeling the linear dynamics are considered, namely Finite Impulse Response (FIR) model with and without prior knowledge, as well as the rational transfer function case. It is shown that the prior information affects the spectrum of the minimum power optimal input. The input with the least power is always obtained for the transfer function model case.