This paper considers the parameter estimation problems of two-input single-output Hammerstein finite impulse response systems. A hierarchical least squares algorithm is proposed for improving the computational efficiency through combining the hierarchical identification principle and the identification model decomposition, and a multi-innovation least squares algorithm is proposed for enhancing the parameter estimation accuracy based on the multi-innovation identification theory. The key is to derive two sub-identification models, each of which contains a set of merged parameter vectors. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the over-parametrization identification method, and can be easily extended to multi-input multi-output systems. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

Hierarchical least squares parameter estimation algorithm for two-input Hammerstein finite impulse response systems

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cta.2019.0112

Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

The Kalman filter is not suitable for the state estimation of linear systems with multistate delays, and the extended state vector Kalman filtering algorithm results in heavy computational burden because of the large dimension of the state estimation covariance matrix. Thus, in this paper, we develop a novel state estimation algorithm for enhancing the computational efficiency based on the delta operator. The computation analysis and the simulation example show the performance of the proposed algorithm.

/pdf/highly-computationally-efficient-state-filter-based-on-the-fevfw5o22u.pdf

Highly computationally efficient state filter based on the delta operator

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

This paper presents the problems of state space model identification of multirate processes with unknown time delay. The aim is to identify a multirate state space model to approximate the parameter-varying time-delay system. The identification problems are formulated under the framework of the expectation maximization algorithm. Through introducing two hidden variables, a new expectation maximization algorithm is derived to estimate the unknown model parameters and the time-delays simultaneously. The effectiveness of the proposed algorithm is validated by a simulation example.

State space model identification of multirate processes with time-delay using the expectation maximization

This paper presents a moving horizon estimation approach for the multirate sampled-data system with unknown time-delay sequence. To estimate the unknown variables of interest, two main challenging issues need to be addressed: (a) synthesizing the multirate input and output data for state estimation, (b) simultaneously estimating the continuous state and discrete time-delay sequence. In this work a moving horizon estimation based approach is developed to tackle these issues. The proposed approach can simultaneously estimate both the continuous states and discrete time-delay sequence for dynamic systems. The effects of different noise level on the estimation of continuous states and discrete time-delay sequence are analyzed. The effectiveness of this method is illustrated through a simulation study.

Moving horizon estimation for multirate systems with time-varying time-delays

An approach for deriving pulse rate variability (PRV) from photoplethysmography (PPG) signals is proposed. By combining sliding window iterative discrete Fourier transform (DFT) with the Hilbert transform algorithm, this method effectively reduces the influence of noise and sampling frequency compared with those of traditional methods. First, the fundamental component of the PPG signal is computed with the sliding window iterative DFT algorithm. Then, it is processed by an integer coefficient low-pass filter and the Hilbert transform to produce an instantaneous pulse rate (IPR). Finally, PRV is extracted from IPR in the frequency domain. PRV is also derived from the PPG signal in the time domain for comparison with that derived using the proposed method. Furthermore, PRV obtained from PPG signals at various sampling frequencies and with various noise levels is investigated. The results show that the proposed method can accurately derive PRV from PPG signals, even if the PPG signal has a low sampling frequency (4 Hz) and high noise (e.g., SNR is about 3.0). The experimental results indicate that the proposed method can be used to estimate PRV when the subjects are in different states (rest, sleeping, or visual fatigue). Moreover, the proposed method is efficient and thus suitable for detecting PRV in real time. It has potential for PRV assessment in various medical fields, such as home health and clinical and hospital environments.

Pulse Rate Variability Estimation Method Based on Sliding Window Iterative DFT and Hilbert Transform

Random noise attenuation is an important step in seismic processing. A method with an adaptive threshold based on scale and directional characteristics of shearlet transform is proposed to attenuate seismic random noise. The method can adaptively calculate threshold according to the decomposed scales and directions, where the directional characteristics are embodied by using the L1 norm of coefficients. Compared to the universal threshold and the threshold based on scale methods only, the scale and directional threshold methods can remove random noise and reserve an effective signal to the utmost extent. The synthetic and field data examples demonstrate that the proposed method can significantly improve the denoising effect and has the stronger denoising ability.

Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the authoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA and the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption and the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for healthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some information from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis in some portable and wearable medical devices.

/pdf/a-real-time-analysis-method-for-pulse-rate-variability-based-1hdmds1h5f.pdf

Yongxin Chou

Papers

State space model identification of multirate processes with time-delay using the expectation maximization

Moving horizon estimation for multirate systems with time-varying time-delays

Pulse Rate Variability Estimation Method Based on Sliding Window Iterative DFT and Hilbert Transform

Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy.