Iterative Parameter Estimation Algorithm Research Articles

Speech Signal Analysis With a Refined Iterative Adaptive Method

A new speech signal analysis method referred to as a refined iterative adaptive method (RIAM) is introduced in this paper. Based on time-varying adaptive sinusoidal modeling, the RIAM method tries to determine in an iterative adaptive manner the instantaneous components of time-varying quasi-periodic multi-component signals such as voiced speech. The proposed method can adjust the current analysis parameters to the time-varying characteristics of the speech signal. This is done using a refined iterative sinusoidal parameter estimation algorithm based on a frequency correction mechanism combined with an adaptive scheme. The experiments on voiced speech demonstrate that the proposed RIAM algorithm outperforms some well-known state-of-the-art approaches. The RIAM algorithm provides a higher signal to reconstruction error ratio (SRER) of 42.267 dB with an improvement of 19.752 dB, 5.054 dB, and 2.552 dB compared to the conventional sinusoidal model (SM), adaptive harmonic model (aHM), and extended adaptive quasi harmonic model (eaQHM), respectively.

Teasing Out the Overall Survival Benefit With Adjustment for Treatment Switching to Multiple Treatments

In oncology clinical trials, characterizing the long-term overall survival (OS) benefit for an experimental treatment is often unobservable if some patients in the control group switch to drugs in the experimental group and/or other cancer treatments after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the experimental treatment on OS that would have been estimated if there were no treatment switches. Several commonly used statistical methods are available to estimate OS benefit while adjusting for treatment switching, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting (IPCW), two-stage model (2-stage), iterative parameter estimation (IPE) algorithm or rank-preserving structural failure time models (RPSFTM). However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments. While some existing methods including RPSFTM and IPE cannot handle complicated scenarios such as multilevel switching, other methods such as IPCW and two-stage methods are restricted with strong assumptions. To address this challenge, we propose two additional methods: stratified RPSFTM and random-forest-based prediction. A simulation study is conducted to assess the performance of the existing methods along with the two newly proposed approaches.

Residual and local influence analyses for unit gamma regressions

AbstractWe obtain local influence measures and residuals for the unit gamma regression model. In particular, we introduce four residuals that are based on Fisher's iterative scoring parameter estimation algorithm and develop local influence analysis based on several different perturbation schemes: cases weighting, response additive perturbation, and covariate(s) additive perturbation. An empirical application in which variables related to education and investment in research and development are used to explain the proportion of nonpoor people in a set of countries is presented and discussed. Residual and local influence analyses show that the unit gamma regression model yields a good fit to the data, even outperforming the beta regression model. The diagnostic analysis singles out countries whose data are worthy of further investigation. Our results reveal that lower poverty levels are associated with higher shares of investment in high technology. The statistical significance of such a relationship is not sensitive to atypical data points.

Spatiotemporal Thermal Field Modeling Using Partial Differential Equations With Time-Varying Parameters

Accurate modeling of a thermal field is one of the fundamental requirements in engineering thermal management in numerous industries. Existing studies have shown that using differential equations to model a thermal field delivers good performance when the parameters are predetermined through physical or experimental analysis. However, due to variations of the inner medium affected by certain latent factors, the parameters in differential equation models may not be treated as constants while the thermal field is estimated, and this fact poses a new challenge to field estimation by directly solving the differential equation models. In this study, a novel approach to thermal field modeling is developed by considering the parameters as functional variables that vary temporally in partial differential equations (PDEs). This approach provides a new perspective to model the dynamic thermal field by fully using the collected sensor data from the thermal system. Specifically, time-varying parameters can be constructed through a combination of basis functions whose coefficients can be efficiently estimated through the sensor data. A two-level iterative parameter estimation algorithm is also tailored to obtain the parameters in the PDE model. Both simulation and real case studies show that our proposed approach provides satisfactory estimation performance compared with the benchmark method that uses the constant parameter estimation. Note to Practitioners —The proposed method aims to model a thermal field using PDEs with time-varying parameters. To better implement this method in practice, three things are noteworthy: first, the proposed method models a thermal field by fully considering physics-specific engineering knowledge using PDEs and the collected sensor data from thermal systems. Second, because time-varying parameters in PDEs cannot be estimated directly, the proposed model represents the time-varying parameters by a combination of B-spline basis functions in terms of time. Estimating time-varying parameters is converted into estimating the constant coefficients of the basis functions. Because the derivatives of a thermal field might not have an analytical expression, the proposed model represents the thermal field by a combination of B-spline basis functions. Taking the derivatives of the thermal field is converted into taking the derivatives of the corresponding basis functions. Third, the proposed method can not only model a thermal field but can also be applied in other physics-specific engineering cases.

Line Impedance Estimation Based on Synchrophasor Measurements for Power Distribution Systems

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible.

Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the information vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.

Iterative Parameter Estimation for Signal Models Based on Measured Data

This paper studies the modeling of multi-frequency signals based on measured data. With the use of the hierarchical identification principle and the iterative search, several iterative parameter estimation algorithms are derived for the signal models with the known frequencies and the unknown frequencies. For the multi-frequency signals, the hierarchical estimation algorithms are derived by means of parameter decomposition. Through the decomposition, the original optimization problem is transformed into the combination of the nonlinear optimization and the linear optimization problems. The simulation results show that the proposed hierarchical algorithms have better performance than the overall estimation algorithms without parameter decomposition.

Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models

This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.

CALGB/ECOG 100104 (Alliance) study: Lenalidomide (LEN) vs placebo (PBO) maintenance (maint) after stem cell transplant (SCT) for patients (pts) with multiple myeloma—Overall survival (OS) and progression-free survival (PFS) adjusted for treatment (tx) crossover (XO).

8037 Background: At a prespecified interim analysis (Dec 2009), the phase 3 CALGB study results surpassed the prespecified superiority boundary (significantly improved PFS for LEN maint vs PBO after SCT) and the majority of PBO arm pts without progressive disease (PD) crossed over to LEN maint. An updated analysis (cutoff Mar 2015), showed significantly longer OS with LEN maint (HR, 0.56; 95% CI, 0.42-0.76). We examined the effect of LEN vs PBO on OS and PFS from randomization, adjusting for XO effects. Methods: The rank-preserving structural failure time model (RPSFTM; Robins, Commun Stat Theory Methods, 1991) was used for XO adjustment; the iterative parameter estimation (IPE; Branson, Stat Med, 2002) algorithm was used as validation. Survival was portioned assuming a residual LEN effect after discontinuation. A landmark analysis was also performed at the Dec 2009 interim for pts who remained on Tx. Results: Pts were randomized to LEN maint (n = 231) and PBO (n = 229) (intent-to-treat [ITT] population); 76 pts without PD crossed over from PBO to LEN. Median time from randomization to XO was 11.5 mos. The relative Tx effect for OS and PFS increased for LEN vs PBO when adjusting for XO using RPSFTM and IPE (Table). The landmark analysis at the Dec 2009 interim (PBO XO, n = 76; No XO, n = 34) showed the Tx effect is not dissimilar to the ITT analysis (HR 0.53; 95% CI, 0.25-1.13). Sensitivity analyses showed consistent results. Conclusions: Adjusting for the potential diluting effects of XO reduced median OS and PFS with PBO, and improved the Tx effect in the ITT analyses for OS and PFS for LEN vs PBO maint after SCT. The statistical significance of the ITT analyses was maintained throughout. Support: U10CA180821, U10CA180882, CA180820. Clinical trial information: NCT00114101. [Table: see text]

Online Parameter Estimation for Model-Based Force Control in Milling Processes

In order to adapt to a milling machine’s changing behavior while operating due to changing tool-workpiece contact conditions adaptive controllers are an advantageous approach to control the machine’s feed rate. Otherwise inaccuracies can occur leading to poor quality. In this paper Model Predictive Control (MPC) is used to control the feed rate of the machine. By applying a parameterized model to the current state of the milling process its future trend can be predicted. An n-th order system with delay time is used to model the behavior of the milling machine’s velocity control loop. By continually re-estimating the model parameters at run time, the controller adapts to the current machine behavior. We developed solutions of the maximum likelihood estimators of the model’s independent parameters for a state-space representation that can be numerically efficiently calculated. These estimators can be used to estimate the parameters of the n-th order system and the delay time iteratively based on the collected process data. Two validation studies, one emulating an online estimation based on real data from the five-axis machining center Mazak Variaxis 630II-T, and one simulation study with a simulated machine, were carried out to validate the iterative parameter estimation algorithm. In these studies, the time variant model, whose parameters are re-estimated on the basis of the developed estimators, is compared to a time invariant model, for which the controller does not adapt to changing machine behavior.

The damping iterative parameter identification method for dynamical systems based on the sine signal measurement

The sine signal is used widely in the signal processing, communication, system analysis and system identification. This paper proposes a damping parameter estimation algorithm for dynamical systems based on the sine frequency response. The measured data are collected by taking the sine signals as the input. Analyzing the system׳s output sine response function, we can construct a nonlinear objective function. By using the nonlinear optimization techniques, we propose an iterative algorithm to estimate the system parameters. In order to overcome the singular or ill-conditioned matrix during the iterative process, we introduce a damping factor in the proposed iterative algorithm. At the same time the gradient iterative parameter estimation algorithm and the Gauss–Newton iterative parameter estimation algorithm are derived for comparing the performance of the presented methods. Moreover, the simulation results given by an example indicate that the proposed method works well.

Adjusting for the Confounding Effects of Treatment Switching-The BREAK-3 Trial: Dabrafenib Versus Dacarbazine.

Patients with previously untreated BRAF V600E mutation-positive melanoma in BREAK-3 showed a median overall survival (OS) of 18.2 months for dabrafenib versus 15.6 months for dacarbazine (hazard ratio [HR], 0.76; 95% confidence interval, 0.48-1.21). Because patients receiving dacarbazine were allowed to switch to dabrafenib at disease progression, we attempted to adjust for the confounding effects on OS. Rank preserving structural failure time models (RPSFTMs) and the iterative parameter estimation (IPE) algorithm were used. Two analyses, "treatment group" (assumes treatment effect could continue until death) and "on-treatment observed" (assumes treatment effect disappears with discontinuation), were used to test the assumptions around the durability of the treatment effect. A total of 36 of 63 patients (57%) receiving dacarbazine switched to dabrafenib. The adjusted OS HRs ranged from 0.50 to 0.55, depending on the analysis. The RPSFTM and IPE "treatment group" and "on-treatment observed" analyses performed similarly well. RPSFTM and IPE analyses resulted in point estimates for the OS HR that indicate a substantial increase in the treatment effect compared with the unadjusted OS HR of 0.76. The results are uncertain because of the assumptions associated with the adjustment methods. The confidence intervals continued to cross 1.00; thus, the adjusted estimates did not provide statistically significant evidence of a treatment benefit on survival. However, it is clear that a standard intention-to-treat analysis will be confounded in the presence of treatment switching-a reliance on unadjusted analyses could lead to inappropriate practice. Adjustment analyses provide useful additional information on the estimated treatment effects to inform decision making. Treatment switching is common in oncology trials, and the implications of this for the interpretation of the clinical effectiveness and cost-effectiveness of the novel treatment are important to consider. If patients who switch treatments benefit from the experimental treatment and a standard intention-to-treat analysis is conducted, the overall survival advantage associated with the new treatment could be underestimated. The present study applied established statistical methods to adjust for treatment switching in a trial that compared dabrafenib and dacarbazine for metastatic melanoma. The results showed that this led to a substantially increased estimate of the overall survival treatment effect associated with dabrafenib.

Modeling and predicting tissue movement and deformation for high intensity focused ultrasound therapy.

PurposeIn ultrasound-guided High Intensity Focused Ultrasound (HIFU) therapy, the target tissue (such as a tumor) often moves and/or deforms in response to an external force. This problem creates difficulties in treating patients and can lead to the destruction of normal tissue. In order to solve this problem, we present a novel method to model and predict the movement and deformation of the target tissue during ultrasound-guided HIFU therapy.MethodsOur method computationally predicts the position of the target tissue under external force. This prediction allows appropriate adjustments in the focal region during the application of HIFU so that the treatment head is kept aligned with the diseased tissue through the course of therapy. To accomplish this goal, we utilize the cow tissue as the experimental target tissue to collect spatial sequences of ultrasound images using the HIFU equipment. A Geodesic Localized Chan-Vese (GLCV) model is developed to segment the target tissue images. A 3D target tissue model is built based on the segmented results. A versatile particle framework is constructed based on Smoothed Particle Hydrodynamics (SPH) to model the movement and deformation of the target tissue. Further, an iterative parameter estimation algorithm is utilized to determine the essential parameters of the versatile particle framework. Finally, the versatile particle framework with the determined parameters is used to estimate the movement and deformation of the target tissue.ResultsTo validate our method, we compare the predicted contours with the ground truth contours. We found that the lowest, highest and average Dice Similarity Coefficient (DSC) values between predicted and ground truth contours were, respectively, 0.9615, 0.9770 and 0.9697.ConclusionOur experimental result indicates that the proposed method can effectively predict the dynamic contours of the moving and deforming tissue during ultrasound-guided HIFU therapy.

A Simultaneous Iterative Learning Control and Dynamic Modeling Approach for A Class of Nonlinear Systems

This paper presents a novel simultaneous iterative learning control and dynamic modeling (SILCDM) approach. For a class of unknown and repeatable nonlinear discrete-time systems, a model-free iterative learning control (ILC) method is applied first. Meanwhile, by using the data generated during the repetitive operations, a novel iterative learning parameter estimation algorithm is proposed to calibrate the unknown time-varying parameter in the nonlinear system with known structure simultaneously. After the model parameter is well identified, the accurate dynamic model of the controlled plant is obtained. With this identified model, the model-free ILC method is then switched to a model-based optimal ILC method in order to get much better control performance. The theoretical analysis shows that the proposed pproach guarantees the convergence of both the system state and the parameter identification. The effectiveness of the SILCDM is further demonstrated through simulations.

Research on Weighted Iterative Stage Parameter Estimation Algorithm of Time Series Model

Time series analysis has been extensively used in many fields, such as system identification, modeling and data predication, and played an important role in system design, planning and performance analysis. The focus of time series application study is how to improve the accuracy and computation speed of the parameter estimation. Many researchers have carried out system modeling study by applying time series analysis and have gained their research results. The traditional methods such as maximum likelihood estimation, moment estimate and least square estimate which exit the defect of low precision, poor convergence and parameter estimation white noises coupling, are mostly utilized in parameter estimation for model. Taking this as basis the data forecasting and anomaly detection are conducted, which is hard to ensure the system’s stability. Different from the traditional algorithm, this paper proposes a new weighted iterative stage parameter estimation algorithm which avoids the coupling with white noise estimation of ARMA model parameter and improves the accuracy of parameter estimation. In theory, this algorithm tends to provide a good convergence performance. The experimental results based on ARIMA model show that the algorithm can improve the accuracy of parameter estimation and provide a good convergence performance.

Abstract CT330: Adjusting for confounding effects of treatment crossover in a randomized phase 2 study of dabrafenib plus trametinib in BRAF V600+ metastatic melanoma

Abstract In a randomized phase 2 study (BRF113220), dabrafenib (150 mg twice daily) plus trametinib (2 mg once daily) improved progression-free survival (HR=0.39; 95% CI, 0.25-0.62; P<.001) vs dabrafenib alone. 54 patients were randomized to each arm. At the most recent data cut (March 2013), median duration of follow-up was 24 months and 52% of patients had died. ITT analyses of overall survival (OS) are likely to underestimate the true benefit of treatment because patients in the control arm (83%) were able to cross over to combination treatment upon progression. ITT median OS, a secondary endpoint, was 23.8 vs 20.2 months (HR=0.73; 95% CI, 0.43-1.24). We adjusted for confounding due to this crossover using randomization-based crossover adjustment methods: the rank-preserving structural failure time model (RPSFTM) and iterative parameter estimation (IPE) algorithm. Different assumptions about the durability of treatment effect were tested: “treatment group” analyses allow for a treatment effect that continues beyond treatment discontinuation, while “on treatment_observed” analyses assumed that the treatment effect disappears upon discontinuation. Results are presented in the Table. Reestimation of median OS following adjustment with both RPSFTM and IPE (possible only for the treatment group analyses) suggested an OS gain of 9.1 months with combination treatment vs dabrafenib alone (compared with a 3.6-month gain under ITT assumptions). The RPSFTM estimation method performed marginally better than the IPE analyses and may provide more robust results. After adjusting for the effect of crossover, the reduction in HRs to 0.47-0.50 suggests that the ITT analysis underestimates the effect of dabrafenib plus trametinib on OS. Results are uncertain due to the limiting assumptions associated with the RPSFTM and IPE methodology, small trial size, low number of observed deaths, and low number of control group patients who did not cross over. Crossover Adjustment MethodAssumptionOS HR (95% CI)None (ITT analysis unadjusted for crossover)0.73 (0.43-1.24)RPSFTMTreatment group0.47 (0.13-1.66)On treatment–observed analyses0.49 (0.15-1.63)IPETreatment group0.50 (0.16-1.54)On treatment–observed analyses0.47 (0.14-1.61) Citation Format: Nicholas Latimer, Mayur Amonkar, Ceilidh Stapelkamp, Peng Sun. Adjusting for confounding effects of treatment crossover in a randomized phase 2 study of dabrafenib plus trametinib in BRAF V600+ metastatic melanoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr CT330. doi:10.1158/1538-7445.AM2014-CT330

Parameter estimation for an input nonlinear state space system with time delay

This paper researches parameter estimation problems for an input nonlinear system with state time-delay. Combining the linear transformation and the property of the shift operator, the system is transformed into a bilinear parameter identification model. A gradient based and a least squares based iterative parameter estimation algorithms are presented for identifying the state time-delay system. The simulation results confirm that the proposed two algorithms are effective and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.

An effective direct closed loop identification method for linear multivariable systems with colored noise

Multivariable control systems with colored noise widely exist in the most industrial fields, while the system identification under the closed loop conditions is needed in many cases. In view of the above two situations, it needs to find a convenient and effective method to solve the problem. Firstly, the design of the external input signals ensures the identifiability of closed loop system. Secondly, to make the direct method feasible for closed loop identification, the noise model selected is reasonably flexible and independently parameterized. On this basis, this paper proposes an improved method combining the direct closed loop identification approach with the iterative least squares parameter estimation algorithm, which can be an practical solution to the closed loop identification of multivariable systems with colored noise. The presented algorithm based hierarchical identification principle has a strong anti-jamming capability to effectively deal with colored noise existed in the system. Finally, the illustrative examples are given to demonstrate the effectiveness and accuracy of the proposed algorithm.

Least-Squares Based and Gradient Based Iterative Parameter Estimation Algorithms for a Class of Linear-in-Parameters Multiple-Input Single-Output Output Error Systems

The identification of a class of linear-in-parameters multiple-input single-output systems is considered. By using the iterative search, a least-squares based iterative algorithm and a gradient based iterative algorithm are proposed. A nonlinear example is used to verify the effectiveness of the algorithms, and the simulation results show that the least-squares based iterative algorithm can produce more accurate parameter estimates than the gradient based iterative algorithm.

Hierarchical gradient based and hierarchical least squares based iterative parameter identification for CARARMA systems

According to the iterative identification technique and the hierarchical identification principle, this paper presents a two-stage gradient based and a least squares based iterative parameter estimation algorithms (i.e., the hierarchical gradient based iterative algorithm and the hierarchical least squares based iterative algorithm) for controlled autoregressive autoregressive moving average systems. The proposed two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm. The simulation results indicate that the two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm.