Orthogonal projection based subspace identification against colored noise

In this paper, a bias-eliminated subspace identification method is proposed for industrial applications subject to colored noise. Based on double orthogonal projections, an identification algorithm is developed to eliminate the influence of colored noise for consistent estimation of the extended observability matrix of the plant state-space model. A shift-invariant approach is then given to retrieve the system matrices from the estimated extended observability matrix. The persistent excitation condition for consistent estimation of the extended observability matrix is analyzed. Moreover, a numerical algorithm is given to compute the estimation error of the estimated extended observability matrix. Two illustrative examples are given to demonstrate the effectiveness and merit of the proposed method.     

An improved constant coefficient multiplication algorithm based on cascaded adder graph

In many digital signal processing algorithms,e.g.,digital filters,the multiplier coefficients are constant.Hence,it is possible to implement the multiplier using shifts,adders,and subtracters.In this work a new algorithm of constant coefficient multiplication with few adders and registers is proposed.This approach is based on cascaded adder graph.In this paper all cascaded adder graph structures for any integer can be derived,and the analytical method for the number of register and adder occupation is given.Through comparison of occupied resources,the optimal adder graph can be obtained.Finally,comparing with previous optimal algorithms,a design example for finite impulse response(FIR) filter confirms the validity and good engineering practicability of this algorithm.     

An application to detect the edge and texture of the flower by canny algorithm

For the edge and texture characteristics of the flower, a new improved canny algorithm is proposed. In the method, canny algorithm detects the edge and the texture of the flower perfectly, the image noise is eliminated simultaneously. The method is applied to all bloom stages. The growing status of the flower could be better analyzed.     

A linear search scheme for effective order of channel impulse response in OFDM systems

This paper deals with the estimation of the channel impulse response （CIR） in orthogonal frequency division multiplexing （OFDM） systems. In particular, we focus on the maximum likelihood （ML） estimator. The ML estimator is simpler to implement as it needs no information on the channel statistics, but its performance depends on the assumed effective order of channel impulse response （EOCIR）. In this paper, we propose a novel ML channel estimator which combines the ML estimation with a strategy of linear search. The proposed method introduces a few complexities, but, compared with the existing ML channel estimators, it is highly robust to variation of EOCIR Its performance has been proved using theoretical analysis and simulations.     

A New Design Approach for Nearly Linear Phase Stable IIR Filter using Fractional Derivative

In this paper,a new design method for digital infinite impulse response(IIR)filters with nearly linear-phase response is presented using fractional derivative constraints(FDCs).In the proposed method,design problem of an IIR frlter is constructed as the minimization of phase error between the desired and designed phase response of an allpass filter(APF)such that the designed lowpass filter(LPF)or highpass frlter(HPF)yields less passband(ep),and stopband errors(es)with optimal stopband attenuation(As).In order to have accurate passband(pb)response,FDCs are imposed on appropriate reference frequency,where the optimality of these FDCs are ensured by using a new greedy based sorting mechanism.The simulated results reflect the efficiency of the proposed method in term of improved passband response along with better transition width.However,small reduction in^is observed within the allowable limit,when compared to noin-fractional design approach,but the designed filter remains immune to wordlength(WL)effect.     

Identification Scheme for Fractional Hammerstein Models With the Delayed Haar Wavelet

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix(HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.     

New results on discrete-time delay systems identification

A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper.The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector.The gradient algorithm is used to deal with the identification problem.The effectiveness of this method is illustrated through simulation.     

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:defense scheme and its optimality

In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimating the key parameters of the joint attack and compensating them in the identification algorithm,a compensation-oriented defense scheme is proposed.Then the identification algorithm of system parameter is designed and is further proved to be consistent.The asymptotic normality of the algorithm is obtained,and on this basis,we propose the optimal defense scheme.Furthermore,the implementation of the optimal defense scheme is discussed.Finally,a simulation example is presented to verify the effectiveness of the main results.     

Identification algorithm based on the approximate least absolute deviation criteria

Considering the situation that the least-squares (LS) method for system identification has poor robustness and the least absolute deviation (LAD) algorithm is hard to construct, an approximate least absolute deviation (ALAD) algorithm is proposed in this paper. The objective function of ALAD is constructed by introducing a deterministic function to approximate the absolute value function. Based on the function, the recursive equations for parameter identification are derived using Gauss-Newton iterative algorithm without any simplification. This algorithm has advantages of simple calculation and easy implementation, and it has second order convergence speed. Compared with the LS method, the new algorithm has better robustness when disorder and peak noises exist in the measured data. Simulation results show the efficiency of the proposed method.     

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to （-1, 1）. Based on the deduced principle of acceleration factors, a new adaptive PSO algorithm- harmonious PSO （HPSO） is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.     

基于固定尺度正交小波包变换的脉冲响应辨识方法研究

小波包变换能作为一种有效工具对信号特性进行精细分析,同时提供具有良好时频局 部性的正交小波包基.本文将Eykhoff脉冲响应辨识方法中的正交函数基取成正交小波基函数, 提出了一种新的脉冲响应辨识方法,该方法不但保证了原来Eykhoff方法的有效性,而且实用性 更好、实用范围更广.仿真结果表明,所提出的脉冲响应辨识算法对于确定性过程和随机过程都具 有很好的辨识结果,具有辨识精度高、克服噪声干扰能力和跟踪系统动态特性变化能力强的优点.     

一种新型的非参数模型及其在系统辨识中的应用

本文给出了一个求解广义正交多项式的微分运算矩阵的新方法，应用对连续线性系统的脉冲响应函数进行正交逼近的方法来讨论脉冲响应函数的实现问题，得到了一类新型的非参数模型，并导出了利用该模型来辨识连续线性系统的脉冲响应函数的算法，最后给出了例子证实本文所给方法的有效性。     

强脉冲噪声环境下的多径衰信道估计

提出一种在强干扰脉冲噪声存在下对无线多径信道进行估计的算法.在无线通信系统中,衰落信道可以采用自回归(AR)模型建模,通过RLS算法和自适应Kalman滤波器分别对AR模型的参数进行估计,但是,这两种算法对噪声干扰非常敏感.为了加快RLS算法的收敛性,并有效抑制大脉冲干扰的影响,在算法的改进中引入了抑制因子,用于对脉冲干扰幅度的抑制.仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估参数的收敛速度.     

加权[lp]范数LMS算法的稀疏系统辨识

针对经典最小均方（LMS）算法没有考虑冲击响应通常具有稀疏性的特点,一般的稀疏LMS算法当自适应趋于稳态时,对小系数施加过大的吸引力,导致稳态误差增大的缺点,提出对稀疏系统进行辨识的改进的[lp(0相似文献   

Identification/reduction to a balanced realization via the extendedimpulse response gramian

A new identification/reduction algorithm for linear, discrete time-invariant (LDTI) systems is proposed which is based on the extended impulse response gramian first defined here for LDTI systems. The reduction algorithm employs singular value decomposition to retain states corresponding to dominant singular values of these gramians. The proven properties of the reduced order models include convergence to a balanced realization with conditional controllability, observability, and asymptotic stability. A suboptimal property of the model (in minimizing an impulse response error norm) is also demonstrated. The proposed technique can handle impulse response data of deterministic or stochastic nature for system identification application     

Impulse response function identification of linear mechanical systems based on Kautz basis expansion with multiple poles

The impulse response function (IRF) identification of linear mechanical systems is important in many engineering applications. This paper proposes a novel IRF identification method of linear systems based on Kautz basis expansion with multiple poles. In order to reduce the parameters to be identified, the IRF is expanded in terms of orthogonal Kautz functions with multiple poles, and the poles in Kautz functions should be optimised. This allows the identification of IRF for linear mechanical systems operated under more than one mode, such as systems under the white noise excitation or the swept frequency excitation with a wide range of frequency, and can improve the identification accuracy. Furthermore, based on the backpropagation through-time technique and the expectation maximisation algorithm, a pole optimisation algorithm is presented in this paper. The simulation studies verify the effectiveness of the proposed IRF identification method.     

一种求解雷达目标冲激响应的并行算法

雷达目标的冲激响应是目标识别中的一种重要手段，可以从冲激响应中提取目标的各种特征，如目标的谐振频率等。为了实时地获得目标的冲激响应，本文提出了一种基于共轭梯度法的并行算法，同时改进了共轭梯度法的终止选代条件。经计算机仿真及对实测数据的计算，证明了这种方法的有效性。     

Non-parametric linear time-invariant system identification by discrete wavelet transforms

We describe the use of the discrete wavelet transform (DWT) for non-parametric linear time-invariant system identification. Identification is achieved by using a test excitation to the system under test (SUT) that also acts as the analyzing function for the DWT of the SUT's output, so as to recover the impulse response. The method uses as excitation any signal that gives an orthogonal inner product in the DWT at some step size (that cannot be 1). We favor wavelet scaling coefficients as excitations, with a step size of 2. However, the system impulse or frequency response can then only be estimated at half the available number of points of the sampled output sequence, introducing a multirate problem that means we have to ‘oversample’ the SUT output. The method has several advantages over existing techniques, e.g., it uses a simple, easy to generate excitation, and avoids the singularity problems and the (unbounded) accumulation of round-off errors that can occur with standard techniques. In extensive simulations, identification of a variety of finite and infinite impulse response systems is shown to be considerably better than with conventional system identification methods.     

Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.     

裁剪频谱的线性系统DFT反卷积算法

在对传统求冲击响应方法的误差进行分析的基础上，提出了一种新的卷积反演算法。该算法通过提高采样率后对频谱进行裁剪，然后运用zoom运算降低采样率，再通过反卷积求出系统的冲击响应。仿真实验证明，该算法能有效地避免量化误差的放大和迁移所造成的影响，比传统方法和最小二乘法具有更小的误差。