无线传感器网络中的节点自适应周期定位

无线传感器网络节点自定位算法是无线传感器网络系统的重要组成部分,是无线传感器网络中所有应用得以实现的基础。基于最小二乘估计的自适应周期定位算法采用周期定位机制控制网络中节点定位,使用基于接收信号强度指示的测距技术获取节点间距离,启动定位周期,直至定位周期终止,完成定位。未知节点采用极大似然估计得到初解,使用最小二乘估计获得自身位置坐标的最终解。仿真实验表明,基于最小二乘估计的自适应周期定位算法能显著提高网络中未知节点的定位率,有效抑制测距误差的传播,提高了节点定位精度。     

IQ不平衡OFDM系统高性能稀疏信道估计算法

针对正交频分复用系统中收发两端都有同相和正交不平衡的问题,本文提出了一种时域最小二乘（Ｔｉｍｅ ｄｏｍａｉｎｌｅａｓｔｓｑｕａｒｅ,ＴＤ-ＬＳ）信道估计算法。在此基础上,为了进一步挖掘无线信道的稀疏特性,又引入稀疏信 号处理理论中的迭代收缩（Ｉｔｅｒａｔｉｖｅｓｈｒｉｎｋａｇｅ,ＩＳ）和平行协调下降（Ｐａｒａｌｌｅｌｃｏｏｒｄｉｎａｔｅｄｅｓｃｅｎｔ,ＰＣＤ）思想,构造了一种联合的信道估计算法：ＴＤ-ＬＳ-ＩＳ-ＰＣＤ。仿真结果表明：采用相同的最小二乘补偿算法时,提出的ＴＤ-ＬＳ 和ＴＤ-ＬＳ-ＩＳ-ＰＣＤ 的误码性能明显优于传统的频域最小二乘算法;同时ＴＤ-ＬＳ-ＩＳ-ＰＣＤ 算法误码性能优于ＴＤ-ＬＳ,逼近理想情况,因此该算法充分挖掘了信道的稀疏特性。     

卡尔曼滤波的存在性

－－本文证明了线性信号模型的卡尔曼滤波与最优加权最小二乘估计的递推算法先进价，进行应用最优加权最小二乘估计的成批算法证明了卡尔曼滤波的存在条件是要求信号模型满足完全可控制条件和完全可观测条件。     

时变时滞系统的参数辨识及自适应控制

基于最小二乘法一类辨识算法的自适应控制,一般只适用于时滞已知且时不变的被控过程,本文提出了一种包括可估计时变时滞在内的参数辨识方法,该方法扩展了最小二乘一类辨识算法及相应的自适应控制的应用范围,文中通过一个实例讨论了该方法在自适应控制中的应用,并谈及下一步的研究工作。     

基于卡尔曼滤波算法的最小二乘拟合及应用

图像处理或在工业控制中经常要用到最小二乘直线拟合,对于有奇异点的直线拟合,传统的最小二乘法拟合误差较大,难以满足较高精度的要求。卡尔曼滤波算法具有最小无偏方差性,能够去除测量系统中的随机误差,将卡尔曼滤波算法与传统最小二乘法结合,建立了一种基于卡尔曼滤波预处理的最小二乘估计的新方法,获得了比传统最小二乘法效果更好的估计结果。试验证明了该方法的有效性和高精度性。     

基于动态遗忘因子最小二乘与EKF的电池SOC估计

电池荷电状态SOC（State Of Charge）作为电池管理系统中尤为重要的一部分,其准确估计成为锂离子电池研究的重点。为了提高动态工况下的SOC估计精度,对锂离子电池等效模型进行分析,基于AIC（赤池信息）准则确定二阶RC电路为等效电路模型,使用递推最小二乘算法对模型参数进行在线辨识,为提高辨识精度,提出了改进带动态遗忘因子递推最小二乘算法,对算法加入遗忘因子,通过电压结果误差实时动态调整算法遗忘因子取值。将递推最小二乘算法和含动态遗忘因子最小二乘算法分别与扩展卡尔曼滤波（EKF）算法进行SOC联合估计,并对比其预测效果,结果表明含有动态遗忘因子最小二乘与EKF联合估计模型具有更高的精度和鲁棒性。     

一种变步长的非约束频域LMS自适应滤波算法

基于非约束频域自适应滤波器的结构，本文提出一种变步长自适应算法，即采用最小二乘法选取最优变步长收敛因子，计算机仿真结果表明，该算法比非约束频域ＬＭＳ算法（ＵＦＬＭＳ）具有更快的收敛速度和更好的收敛精度。     

多变量系统自适应跟踪的对数律

将文献[1]的基于最小二乘算法的自适应最小方差控制的已有结果推广到更一般的多变量系统。首先找出了保证文献[1]的结论（2．3）和（2．5）对多变量系统成立的条件,然后证明了（n/log n)Rn（Rn=1nn∑i=1｜yi-yi-wi｜^2）的极限存在且有限,这精确地刻画了多变量系统自校正调节器的收敛速度。     

基于观测器的飞行器时变控制效益估计算法

研究飞行器时变控制效益的在线估计方法,证明了系统控制效益可估计的充分条件.针对效益矩阵分段连续时变的情形,给出了基于观测器、方程求解和最小二乘法的在线估计方法,分析和证明了算法的收敛性.数值仿真结果表明,该算法能快速估计时变控制效益,具有良好的稳定性和鲁棒性.     

基于最小二乘法的冗余信息数据融合算法实现

为了有效融合多传感器冗余系统量测信息,使状态的估计值更接近于状态的真实值,实现高精度和高可靠性的状态估计,采取了基于最优加权的最小二乘算法、有限窗加权的最小二乘算法和自学习加权最小二乘算法,分别对多传感器实测数据进行融合处理,融合后数据的方差大幅度降低,估计精度显著提高。并与传统的最小二乘算法进行了仿真对比,结果表明,这3种方法较最小二乘算法融合精度更高,其中,自学习加权的最小二乘融合算法既考虑了历史数据的作用,又考虑了环境噪声和新的采样值的影响,增强了对噪声检测的敏感性,估计效果较好。     

基于神经元网和带死区的最小二乘算法的非线性离散时间系统的自适应控制

针对非线性离散时间系统,提出了一种用带死区的最小二乘算法去调节神经网参数的算法,同其他算法相比,这种算法具有非常高的收敛速度.对于这种自适应控制算法,证明了闭环系统的所有信号是有界的,跟踪误差收敛到以零为原点的球中.     

GLOBAL CONVERGENCE OF MINIMUM VARIANCE SELF‐TUNING SCHEME BASED UPON DAMPED LEAST SQUARES

This paper presents an analysis of the stability and convergence of a damped least squares identification algorithm and establishes the global convergence of a minimum variance self‐tuning scheme based upon damped least squares. The results mathematically demonstrate that the damped least squares can generally be applied to achieve system identification and adaptive control.     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

Stochastic adaptive control using a modified least squares algorithm

Recent papers on stochastic adaptive control have established global convergence for algorithms using a stochastic approximation iteration. However, to date, global convergence has not been established for algorithms incorporating a least squares iteration. This paper establishes global convergence for a slightly modified least squares stochastic adaptive control algorithm. It is shown that, with probability one, the algorithm will ensure that the system inputs and outputs are sample mean square bounded and the mean square output tracking error achieves its global minimum possible value for linear feedback control.     

Recursive exponentially weighted PLS and its applications to adaptive control and prediction

A new and fast recursive, exponentially weighted PLS algorithm which provides greatly improved parameter estimates in most process situations is presented. The potential of this algorithm is illustrated with two process examples: (i) adaptive control of a two by two simulated multivariable continuous stirred tank reactor; and (ii) updating of a prediction model for an industrial flotation circuit. The performance of the recursive PLS algorithm is shown to be much better than that of the recursive least squares algorithm. The main advantage of the recursive PLS algorithm is that it does not suffer from the problems associated with correlated variables and short data windows. During adaptive control, it provided satisfactory control when the recursive least squares algorithm experienced difficulties (i.e., ‘blew’ up) due to the ill-conditioned covariance matrix, (X^TX)_t. For the industrial soft sensor application, the new algorithm provided much improved estimates of all ten response variables.     

基于多分割和口袋方法的最小二乘分类算法

提出了一种改进的最小二乘分类算法,该算法首先利用最小二乘算法对两类数据分类,然后计算每类的中心点,过中心点作已得到的分类线(面)的平行线(面),保留所作平行线(面)之间及线(面)上的数据,剔除其余数据,对剩余数据重新利用最小二乘算法分类,如此循环.在循环过程中利用口袋方法记录下具有最好的分类效果的分类线(面),循环结束后口袋中即为最佳分类线(面).实验结果表明,该算法有效的解决了原有最小二乘分类算法的缺陷,有着良好的分类效果.     

Kernel-based least squares policy iteration for reinforcement learning.

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.     

Adapting the search vector for direct adaptive control systems

In adaptive control algorithms, the adaptation routine (e.g., least squares or gradient) is usually used to adjust the controller parameters to approximate the ideal controller that is assumed to exist. Searching for the ideal parameter vector, a gradient-based hybrid adaptive routine is used here for continuous-time nonlinear systems. The adjustment of the parameter vector is usually based on minimizing the squared error. For direct adaptive control, in this paper an algorithm is presented to adapt the direction of the search vector so that the instantaneous control energy is minimized. Hence, the overall adaptive routine minimizes not only the squared error but also the instantaneous control energy. Stability results of the presented algorithm show that boundedness of the error is dependent on the length of the search vector.     

Online policy iteration algorithm for optimal control of linear hyperbolic PDE systems

In this paper, the linear quadratic (LQ) optimal control problem is considered for a class of linear distributed parameter systems described by first-order hyperbolic partial differential equations (PDEs). Reinforcement learning (RL) technique is introduced for adaptive optimal control design from the design-then-reduce (DTR) framework. Initially, a policy iteration (PI) algorithm is proposed, which learns the solution of the space-dependent Riccati differential equation (SDRDE) online without requiring the internal system dynamics of the PDE system. To prove its convergence, the PI algorithm is shown to be equivalent to an iterative procedure of a sequence of space-dependent Lyapunov differential equations (SDLDEs). Then, the convergence is established by showing that the solutions of SDLDEs are a monotone non-increasing sequence that converges to the solution of the SDRDE. For implementation purpose, an online least-square method is developed for the approximation of the solutions of the SDLDEs. Finally, the proposed design method is applied to the distributed control of a steam-jacketed tubular heat exchanger to illustrate its effectiveness.     

Blind Capon-like adaptive ST-BC MIMO-CDMA receiver based on constant modulus criterion

In this paper, we present a new Capon-like blind receiver based on linearly constrained constant modulus (LCCM) criterion for the multiple-input multiple-output (MIMO) antennas system along with space–time block code (ST-BC) using direct-sequence code division multiple access (CDMA) modulation technique. A time-varying channel model with generalized sidelobe canceller (GSC) associated with the recursive least squares (RLS) algorithm is implemented to reduce the complexity of receiver design. In our derived algorithm, the parameter of constant modulus, α, relating to the desired user power is updated adaptively via stochastic gradient algorithm to track user?s amplitude variation. Also we prove theoretically that in the two-branch filter bank receiver design the weight vector of one branch can be updated simply using the other one, which has been obtained with our proposed CM-GSC-RLS algorithm, with simple pre-calculated transform. Hence computation complexity of the proposed adaptive blind receiver can be further reduced significantly. Via intense simulations it reveals that our proposed scheme has robust performance against the user?s acquisition inaccuracies comparing with current available algorithms.