Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

Research on SINS Error Recursive Model of Ballistic Missile

The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled INS. In the paper, a discrete error recursive model was obtained by studying the applied SINS error model of ballistic missile, and the discrete Kalman filtering simulation based on the model was carried out. The simulated results show that the model can depict the SINS error exactly and provide the advantages for research on integrated guidance and improved hit accuracy.

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

A New Recursive Parameter Estimation Algorithm of Multi－Variable Time－Varying AR Model

A new recursive algorithm of multi variable time varying AR model is proposed. By changing the form of AR model, the parameter estimation can be regarded as state estimation of state equations. Then the Kalman filter is used to estimate the variation of

A RECURSIVE ALGORITHM AND ITS CONVERGENCE FOR PARAMETER ESTIMATION OF CONVOLUTION MODEL

In this article, the problem on the estimation of the convolution model parameters is considered. The recursive algorithm for estimating model parameters is introduced from the orthogonal procedure of the data, the convergence of this algorithm is theoretically discussed, and a sufficient condition for the convergence criterion of the orthogonal procedure is given. According to this condition, the recursive algorithm is convergent to model wavelet A- = （1, α1,..., αq）.

Method of multi-level recursive and application to nonlinear dynamic deformation forecasting

The time-dependence bilinear mixed-regression deformation model and time-dependence bilinear dynamic system deformation model are established for deformation observation series. According to the multi- level recursive method, the time-dependence parameters are first traced and predicted, and then the dynamic system states. Due to the method considering time-dependence of deformation and having stronger adaptability to time-dependence system, it can improve forecast’s precision. It is very effective for data processing of nonlinear dynamic deformation monitoring to make multi-step forecasting.

Some Properties of a Recursive Procedure for High Dimensional Parameter Estimation in Linear Model with Regularization

Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed to overcome the difficulties with high dimension of the observation vector in computation of a statistical regularized estimator. As to deal with high dimension of the vector of unknown parameters, the regularization is introduced by specifying a priori non-negative covariance structure for the vector of estimated parameters. Numerical example with Monte-Carlo simulation for a low-dimensional system as well as the state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of the proposed approach.

A Parameterization of Cooling Rate Calculation under the Non-LTE Condition: Multi-Level Model

Calculations of cooling rate by CO2 15 μm band in the earth's upper mesosphere and lower thermosphere be-come very difficult because of the non-LTE. This is primarily due to the nonlinear vibration-vibrational (VV) transition processes between CO, molecules in different states. This paper suggests that the non-LTE source function be parameterized as a linear combination of two limiting source functions. One limiting source function neglects the VV transitions while the other limiting source function assumes VV transitions being dominant. These two limiting source functions can be derived by linear models. The parameterization schemes proposed here can be applied to the general circulation models including those non-LTE regions.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

Stance detection is the task of attitude identification toward a standpoint.Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting.Moreover,because the target is not always mentioned in the text,most methods have ignored target information.In order to solve these problems,we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory(LSTM)and the excellent extracting performance of convolutional neural networks(CNNs).The method can obtain multi-level features that consider both local and global features.We also introduce attention mechanisms to magnify target information-related features.Furthermore,we employ sparse coding to remove noise to obtain characteristic features.Performance was improved by using sparse coding on the basis of attention employment and feature extraction.We evaluate our approach on the SemEval-2016Task 6-A public dataset,achieving a performance that exceeds the benchmark and those of participating teams.

Multi-level biomechanical models for rehabilitation engineering

Rehabilitation engineering aims in the upmost degree to restore the lost functions for those persons with physical disability. Biomechanical modeling has been widely used for different purposes in rehabilitation engineering to understand the bio-

Effect of Aluminum Hydroxide Adjuvant on the Immunogenicity of the 2009 Pandemic Influenza A/H1N1 Vaccine:Multi-level Modeling of Data with Repeated Measures

Objective To evaluate the effect of the aluminum hydroxide （Al-OH） adjuvant on the 2009 pandemic influenza A/H1N1 （pH1N1） vaccine. Methods In a multicenter, double-blind, randomized, placebo-controlled trial, participants received two doses of split-virion formulation containing 15 ug hemagglutinin antigen, with or without aluminum hydroxide （N-OH）. We classified the participants into six age categories （〉61 years, 41-60 years, 19-40 years, 13-18 years, 8-12 years, and 3-7 years） and obtained four blood samples from each participant on days 0, 21, 35, and 42 following the first dose of immunization. We assessed vaccine immunogenicity by measuring the geometric mean titer （GMT） of hemagglutination inhibiting antibody. We used a two-level model to evaluate the fixed effect of aluminum Al-OH and other factors, accounting for repeated measures. Results The predictions of repeated measurement on GMTs of formulations with or without Al-OH, were 80.35 and 112.72, respectively. Al-OH significantly reduced immunogenicity after controlling for time post immunization, age-group and gender. Conclusion The Al-OH adjuvant does not increase but actually reduces the immunogenicity of the split-virion pH1N1 vaccine.

Recursive dynamics simulator(ReDySim):A multibody dynamics solver

Recursive formulations have significantly helped in achieving real-time computations and model-based control laws. The recursive dynamics simulator （ReDySim） is a MATLAB-based recur- sive solver for dynamic analysis of multibody systems. ReDySim delves upon the decoupled natural orthogonal complement approach originally developed for serial-chain manipulators. In comparison to the commercially available software, dynamic analyses in ReDySim can be performed without creating solid model. The input parameters are specified in MATLAB environment. ReDySim has capability to incorporate any control algorithm with utmost ease. In this work, the capabilities of ReDySim for solving open-loop and closed-loop systems are shown by examples of robotic gripper, KUKA KR5 industrial manipulator and four-bar mechanism. ReDySim can be downloaded for free from http：//www.redysim.co.nr and can be used almost instantly.

Parameter-driven Level of Detail Derivation Method for Semantic Building Facade Model

The relentless progress in the research of geographic spatial data models and their application scenarios is propelling an unprecedented rich Level of Detail(LoD)in realistic 3D representation and smart cities.This pursuit of rich details not only adds complexity to entity models but also poses significant computational challenges for model visualization and 3D GIS.This paper introduces a novel method for deriving multi-LOD models,which can enhance the efficiency of spatial computing in complex 3D building models.Firstly,we extract multiple facades from a 3D building model(LoD3)and convert them into individual semantic facade models.Through the utilization of the developed facade layout graph,each semantic facade model is then transformed into a parametric model.Furthermore,we explore the specification of geometric and semantic details in building facades and define three different LODs for facades,offering a unique expression.Finally,an innovative heuristic method is introduced to simplify the parameterized facade.Through rigorous experimentation and evaluation,the effectiveness of the proposed parameterization methodology in capturing complex geometric details,semantic richness,and topological relationships of 3D building models is demonstrated.

Quantum Realities and Observer-Dependent Universes: An Advanced Observer Model

This paper presents a novel observer model that integrates quantum mechanics, relativity, idealism, and the simulation hypothesis to explain the quantum nature of the universe. The model posits a central server transmitting multi-media frames to create observer-dependent realities. Key aspects include deriving frame rates, defining quantum reality, and establishing hierarchical observer structures. The model’s impact on quantum information theory and philosophical interpretations of reality are examined, with detailed discussions on information loss and recursive frame transmission in the appendices.

基于递归本征正交分解与强跟踪扩展卡尔曼滤波的结构损伤识别

针对目前已有损伤识别方法难以实时跟踪结构损伤且计算量大的问题,提出了一种基于递归本征正交分解(recursive proper orthogonal decomposition,RPOD)与强跟踪扩展卡尔曼滤波(strong tracking extended Kalman filter,STEKF)相结合的模型降阶与结构损伤在线识别方法,对动载荷作用下的结构损伤识别进行了研究。利用RPOD方法在线更新并实时建立反映结构状态的降阶模型,解决未知载荷作用下多自由度结构动力分析计算量大且难以收敛的问题,同时跟踪损伤的演化并对其进行定位;通过STEKF方法跟踪降阶模型的状态向量,识别因损伤而退化的降阶模型参数。分别采用六层剪切型框架的数值模拟与三层钢框架的模型试验验证了该方法的可行性,结果表明,所提出的方法能够准确建立降阶模型并跟踪降阶模型参数的时变历程,同时可以有效地识别出剪切型建筑结构损伤的位置和程度,即使在处理高程度噪声时仍有较高的精度。

An Effective Multiple Model Least Squares Method in Tracking of a Maneuvering Target

A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.

OFDM blind channel estimation based on polynomial models

A two-dimensional （2-D） polynomial regression model is set up to approximate the time-frequency response of slowly time-varying orthogonal frequency-division multiplexing （OFDM） systems. With this model the estimation of the OFDM time-frequency response is turned into the optimization of some time-invariant model parameters. A new algorithm based on the expectation-maximization （EM） method is proposed to obtain the maximum-likelihood （ML） estimation of the polynomial model parameters over the 2-D observed data. At the same time, in order to reduce the complexity and avoid the computation instability, a novel recursive approach （RPEMTO） is given to calculate the values of the parameters. It is further shown that this 2-D polynomial EM-based algorithm for time-varying OFDM （PEMTO） can be simplified mathematically to handle the one-dimensional sequential estimation. Simulations illustrate that the proposed algorithms achieve a lower bit error rate （BER） than other blind algorithms.

River channel flood forecasting method of coupling wavelet neural network with autoregressive model

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

基于变参递归网络和递归最小二乘的连续体机器人控制

连续体机器人通常由柔性材料制成,能够承受大幅度形变,在各领域具有广阔的应用前景.然而,其软体结构和非传统的驱动机制也带来了诸多非线性因素,使得其状态和运动难以被精确建模.因此,为连续体机器人设计了一种无模型控制方案.该方案一方面通过变参递归神经网络(Varying parameter-recursive neural network,VP-RNN)求解连续体机器人的逆运动学,以实现高精度运动控制,另一方面使用递归最小二乘法(Recursive least square,RLS)基于实时数据估计和更新机器人雅可比矩阵伪逆,以避免机器人的解析建模.最后,通过仿真模拟和实物实验验证了所提出控制方案的可行性、精确性和鲁棒性,并通过一系列对比实验突出了所提出方法的优势.该方法率先研究基于递归最小二乘法的连续体机器人雅可比矩阵伪逆估计,对未来的连续体机器人研究具有一定的启示作用.