基于回溯搜索算法的导联选择脑机接口研究

在脑机接口(BCI)中,传统的共空域模式(CSP)算法在提取特征信号与事件相关去同步/同步(ERD/ERS)的信息上得到了很好的效果。但是CSP算法受限于电极导联数、EEG信号的时间段和频带等因素,如电极导联数的增加,CSP算法容易过拟合,数据记录容易混乱,使得运算变得复杂,增加运算时间,降低数据分类正确率。所以,CSP算法存在局限性。使用回溯搜索优化算法(BSA)能够为CSP算法自动挑选出一组导联数组子集,并且以分类错误率作为BSA算法的目标函数进行实验。实验采用两类实验数据(第三、四届国际BCI竞赛数据集)进行交叉验证分类实验。实验结果表明,两类数据的导联数目大幅度减少,分类正确率有所提高。     

Identification of Hammerstein models with cubic spline nonlinearities

This paper considers the use of cubic splines, instead of polynomials, to represent the static nonlinearities in block structured models. It introduces a system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, where cubic splines represent the static nonlinearity and the linear dynamics are modeled using a finite impulse response filter. The algorithm uses a separable least squares Levenberg-Marquardt optimization to identify Hammerstein cascades whose nonlinearities are modeled by either cubic splines or polynomials. These algorithms are compared in simulation, where the effects of variations in the input spectrum and distribution, and those of the measurement noise are examined. The two algorithms are used to fit Hammerstein models to stretch reflex electromyogram (EMG) data recorded from a spinal cord injured patient. The model with the cubic spline nonlinearity provides more accurate predictions of the reflex EMG than the polynomial based model, even in novel data.     

Blind identification of LTI-ZMNL-LTI nonlinear channel models

A simple method is proposed for blind identification of discrete-time nonlinear models consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). The linear subsystems are allowed to be of nonminimum phase (NMP), though the first LTI can be completely identified only if it is of minimum phase. With a circularly symmetric Gaussian input, the linear subsystems can be identified using simple cepstral operations on a single 2-D slice of the N+1 th-order polyspectrum of the output signal. The linear subsystem of an LTI-ZMNL model can be identified using only a 1-D moment or polyspectral slice if it is of minimum phase. The ZMNL coefficients are not identified and need not be known. The order N of the nonlinearity can, in principle, be estimated from the output signal. The methods are analytically simple, computationally efficient, and possess noise suppression characteristics. Computer simulations are presented to support the theory     

A system identification algorithm using orthogonal functions

An adaptive filter (ADF) structure is proposed for applications in which large-order ADFs are required. It is based on modeling the impulse response of the system to be identified as a linear combination of a set of discrete Legendre orthogonal functions. The proposed adaptive filter structure has desirable stability features and a unimodal mean-square error surface as well as a modular structure that permits an easy increase of the filter order without changing the previous stages. Computer simulations in which the proposed structure is used to identify actual acoustic echo path impulse responses show that the Legendre ADF has better convergence performance than the transversal ADF when identifying systems with long impulse response     

Memoryless nonlinear system identification with unknown model order

The problem of identifying a general memoryless input/output system from measurements of inputs and the corresponding outputs is considered. The measured output is sought to be represented as the linear combination of known functions of the input with some additive noise. The choice of model order to be used to fit the data is the main issue addressed, and a cost function involving the prediction error and the model order is derived. The cost function under certain approximations is shown to be similar to one obtained by H. Akaike (1969, 1970). If there is a real system generating the data, it is shown that the expected value of this cost function is always minimized at the true value of the order as long as the noise variance satisfies certain conditions. Asymptotic results for some cases are derived. An efficient algorithm is proposed for identifying the model order. Some simulation results using the proposed algorithm are also presented     

Stochastic gradient based third-order Volterra system identification by using nonlinear Wiener adaptive algorithm

The nonlinear Wiener stochastic gradient adaptive algorithm for third-order Volterra system identification application with Gaussian input signals is presented. The complete self-orthogonalisation procedure is based on the delay-line structure of the nonlinear discrete Wiener model. The approach diagonalises the autocorrelation matrix of an adaptive filter input vector which dramatically reduces the eigenvalue spread and results in more rapid convergence speed. The relationship between the autocorrelation matrix and cross-correlation matrix of filter input vectors of both nonlinear Wiener and Volterra models is derived. The algorithm has a computational complexity of O(M/sup 3/) multiplications per sample input where M represents the length of memory for the system model, which is comparable to the existing algorithms. It is also worth noting that the proposed algorithm provides a general solution for the Volterra system identification application. Computer simulations are included to verify the theory.     

Hysteresis identification and compensation using a genetic algorithm with adaptive search space

A genetic algorithm with adaptive search space (GAASS) is proposed and applied to identify the hysteresis model parameters of an electromechanical-valve actuator installed on a pneumatic system. According to the normalized fitness distance in each generation, the proposed GAASS method consistently identifies the best search domains in the parameter space and adjusts the crossover and mutation rates in order to achieve fast convergence and high accuracy. Experiments have been conducted to investigate the effectiveness of the proposed hysteresis identification approach. Three different types of sensors have been used in the experimental studies, and the experimental results have demonstrated the effectiveness of the proposed approach.     

一种改进的小波变换抗箔条干扰算法

释放箔条对抗反舰导弹是一种技术简单、成本低廉却效果显著的电子干扰措施。现有反舰导弹末制导雷达系统中的抗箔条干扰方法存在着诸多缺点，如成本高、技术复杂和效果不明显等。小波变换是一种根据目标与箔条云回波信号细节系数相关性的不同而将二者进行区分的算法，具有识别效果好、算法快速和适用范围广等优点。本文对原有的小波变换方法进行了修正，计算机仿真实验证明了该算法的有效性。     

Blind identification of quadratic nonlinear models using neuralnetworks with higher order cumulants

A novel approach to blindly estimate kernels of any discrete- and finite-extent quadratic models in higher order cumulants domain based on artificial neural networks is proposed in this paper. The input signal is assumed an unobservable independently identically, distributed random sequence which is viable for engineering practice. Because of the properties of the third-order cumulant functions, identifiability of the nonlinear model holds, even when the model output measurement is corrupted by a Gaussian random disturbance. The proposed approach enables a nonlinear relationship between model kernels and model output cumulants to be established by means of neural networks. The approximation ability of the neural network with the weights-decoupled extended Kalman filter training algorithm is then used to estimate the model parameters. Theoretical statements and simulation examples together with practical application to the train vibration signals modeling corroborate that the developed methodology is capable of providing a very promising way to identify truncated Volterra models blindly     

时滞时变对象参数辨识方法

针对时滞时变系统,提出了一种参数辨识的新方法.该算法一方面采用互相关函数来辨识滞后时间,并引进了快速傅里叶变换及其反变换,提高了计算效率;另一方面,在变参数增量估计递推最小二乘算法估计时变参数的基础上,引入误差级序列,改善了时变参数的辨识精度.最后通过仿真验证了该方法的良好性能.     

A spatiotemporal super-resolution algorithm for a hybrid stereo video system

This paper presents a spatiotemporal super-resolution method to enhance both the spatial resolution and the frame rate in a hybrid stereo video system. In this system, a scene is captured by two cameras to form two videos, including a low spatial resolution with high-frame-rate video and a high spatial resolution with low-frame-rate video. For the low-spatial-resolution video, the low-resolution frames are spatially super-resolved by the high-resolution video via the stereo matching, the bilateral overlapped block motion estimation, and the adaptive overlapped block motion compensation algorithms, while for the low-frame-rate video, those missed frames are interpolated using the high-resolution frames obtained by fusing the disparity compensation and the motion compensation frame rate up-conversion. Experimental results demonstrate that the proposed mixed spatiotemporal super-resolution method has a more significant contribution to both the subjective and objective qualities than the pure spatial super-resolution or the frame rate up-conversion.     

图像处理技术在火焰目标提取中的应用

在火灾识别系统中,为了精确地提取火焰目标,本文提出了一种综合有效的算法,该算法采用二维最大熵自动阈值法对火灾图像进行了分割处理,随后采用区域生长法,检测出火灾图像中的可疑区域,并以区域生长后的目标区域为模板,采用Otsu阈值选取法对当前图像进行精确分割,提取准确的火焰目标。实验证明该算法能够有效地提取出连续图像序列中的火焰目标图像。     

A two-path tree search algorithm for use with DFE

The two-path tree search is a detection algorithm that improves the performance of standard decision-feedback equalizer detectors by examining two likely paths through the decision tree. The method achieves about 1-dB improvement over standard decision-feedback equalization for typical magnetic recording channels used in hard disk drives. The implementation complexity is modest and is compared with other improved decision-feedback structures     

An efficient algorithm for the extraction of parameters with high confidence from nonlinear models

Analytical models with parameters numerically extracted from I-V data have been used in simulation of MOS circuits. The equations are quasi-physical and the extracted parameters do not normally relate to any single identifiable physical mechanism. We have developed an extraction system that can provide a measure of the level of confidence in the extracted parameters; hence, these parameters may be reliably used in circuit simulation as well as process control. The algorithm described is model independent and can be used for any nonlinear least-squares parameter extraction problem.     

The implementation of nonlinear dynamical systems with wavelet network

A dynamic wavelet network circuit implementation for modelling the nonlinear dynamical networks has been proposed in this study. The dynamical wavelet network includes static wavelet network with Mexican hat wavelet function, the voltage-controlled switches and capacitors. The circuit simulations have been done in Spice for the period-1 limit cycle, the spiral and double scroll attractors of the Chua's circuit.     

基于小波变换的遗传算法在红外跟踪系统中的应用

目前红外跟踪技术是当代红外技术的重要军事应用之一,但由于外界的干扰环境及红外热探测器件本身的灵敏度影响了其成像质量。为了提高红外跟踪系统的图像识别精度,本文针对红外探测器生成的空中目标图像,提出用基于小波变换的遗传算法对红外热图像进行处理。实验结果验证该算法可以有效提高图像质量,能够满足跟踪系统的精度要求。     

LTE系统中小区搜索定时同步算法研究

介绍了LTE系统主同步信号的产生,提出了一种新的符号定时同步算法。利用主同步信号在时域具有镜像对称的特性,结合分段相关同步算法的优点,以及对接收端信号进行累积处理的方法,有效地提高了算法的抗频偏性能和同步性能。同时,将3组本地PSS序列相加后在与接收信号序列进行互相关,有效地减小了算法的运算量并能快速实现下行定时同步。理论分析和仿真实验结果表明,所提出的算法在AWGN信道下具有良好的性能,能够满足LTE系统对同步性能的要求。     

A model selection method for nonlinear system identification based FMRI effective connectivity analysis

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.     

Recursive nonlinear system identification by a stochastic gradient algorithm: stability, performance, and model nonlinearity considerations

A parameter estimation problem in a class of nonlinear systems is considered where the input-output relation of a nonlinear system is approximated by a polynomial model (e.g., a Volterra series). A least mean squares (LMS) type algorithm is utilized for the recursive estimation of the polynomial coefficients, and its resulting mean square error (MSE) convergence properties are investigated. Conditions for the algorithm stability (in the mean square sense) are established, steady-state MSE bounds are obtained, and the convergence rate is discussed. In addition, modeling accuracy versus steady-state performance is examined; it is found that an increase of the modeling accuracy may result in a deterioration of the asymptotic performance, that is, yielding a larger steady-state MSE. Linear system identification is studied as a special case.