一种新的超稳定自适应ⅡR滤波算法

本文利用时变非线性系统的超稳定理论,提出了一种新的自适应ⅡR滤波算法。这种算法既无需选择传统超稳定自适应算法的补偿滤波系数,又无需对信号向量的下界要求条件。计算机仿真实验证实了算法的可行性。     

鲁棒频域样条优先自适应滤波算法及性能分析

样条自适应滤波结构由线性滤波器和样条插值机制级联组成，是解决Wiener-Hammerstein模型系统辨识的一类有效方案。在非线性系统辨识问题中，随着滤波器阶数增加，将增大时域样条自适应滤波算法的计算复杂度，造成计算效率的降低，且系统附加的非Gaussian噪声会对最小均方算法的样条自适应滤波器性能造成不良影响，导致算法的性能恶化甚至失效。为处理非Gaussian噪声干扰和提高长脉冲响应系统辨识的计算效率，本文结合最大熵准则和频域策略应用于样条自适应滤波器中，并在样条自适应滤波结构中分别采用不同的误差信号对线性部分和非线性部分进行优化，提出了一种鲁棒频域样条优先自适应滤波算法。该算法在滤波前利用非线性系统辨识的不变性原理对未知系统进行优先的有限脉冲响应辨识，可提高非线性系统辨识的精度；通过最大熵准则使算法在非Gaussian噪声环境下具有稳健性，以降低更新过程对大异常值的敏感性；并将线性卷积和线性相关运算通过重叠存储的快速Fourier变换方式进行计算，显著提升了算法的计算效率。此外，本文对所提出的自适应算法进行了收敛性和稳态性能分析，并推导出该算法的理论稳态额外均方误差。最后，通过...     

基于自适应Volterra滤波器的非线性系统辨识

为了识别非线性系统的参数,本文提出了一种基于自适应Volterra滤波器的非线性系统辨识方法。给出了自适应Volterra滤波器的LMS和RLS算法。数值仿真结果表明,该方法对于非线性定常和时变系统都有效。     

基于BSNN-ARX的光伏逆变器模型辨识

光伏逆变器是光伏并网系统的核心部件,将基于Hammerstein模型的非线性系统辨识方法引入到光伏并网逆变器的建模中,把单相光伏并网逆变器视为双输入单输出的非线性黑箱系统。在Hammerstein模型的静态非线性环节采用B样条神经网络,动态线性环节采用ARX模型,同时采用基于误差学习准则和最小二乘递归准则的自适应学习方法。实验测试结果表明,提出的BSNN-ARX光伏逆变器模型辨识方法可以对不同天气条件下的逆变器输出功率进行高精度的辨识,从而为并网逆变器的建模提供一种有效途径。     

基于模糊自适应PI的无刷直流电机调速系统研究

针对采用传统PI控制算法的无刷直流电机(BLDCM)调速系统存在精度低、抗干扰能力弱等问题,提出一种基于初始比例值优化的模糊自适应PI控制算法。建立BLDCM转速、电流双闭环调速系统数学模型,对其转速环进行模糊自适应PI控制,并提出一种初始比例值优化的方法。应用Matlab/Simulink进行系统设计和仿真,对比传统PI、普通模糊自适应PI和优化后模糊自适应PI三种控制算法的仿真结果。结果表明,优化后的模糊自适应PI控制算法使BLDCM调速系统具有更好的动态性能,达到了较好的控制效果。     

DVD光学头的BP神经网络自适应PID控制器设计

搭建了一套基于DVD聚焦循道伺服技术和双光子吸收三维光存储技术的双光头三维光盘存储系统.针对DVD光学读取头传递函数,采用BP神经网络自适应PID控制算法,构建PID控制系统仿真模型,通过BP神经网络的超强自学习和非线性逼近能力在线调整PID控制器参数,并进行计算机Matlab仿真.仿真结果表明,BP神经网络自适应PID控制算法提升了系统的响应速度,减小了系统的超调量.     

通信开关电源系统中变换器的智能控制设计

本文讲述了有关通信开关电源系统中变换器的智能控制设计工作。同时,根据变换器时变系统以及非线性特点自适应提出了PID控制算法。     

一种用于非线性复杂系统辨识的自适应模糊神经网络

该文提出一种用于复杂的非线性未知系统辨识的混合神经网络模型自适应模糊神经网络(AFNN)。AFNN网络结构简洁,具有通用逼近的特性,能够克服由于突变点的存在而对系统辨识所带来的误差,提高整个系统的辨识精度。对空空导弹攻击区辨识的仿真结果验证了AFNN网络的有效性。     

基于误差变化率的自适应反应传播算法及其应用

本文针以ＢＰ算法存在收敛速度慢的特点，提出了一种基于网络动态训练误差变化率自动校正学习步长和冲量因子的自适应反向传播算法，异或问题，非线性系统和参数波动系统辨识的结果表明，该方法具有较快的收敛速度。     

基于误差变化率的自适应反向传播算法及其应用

本文针对BP算法存在收敛速度慢的缺点，提出一种基于网络动态训练误差变化率自动校正学习步长和冲量因子的自适应反向传播算法.异或问题、非线性系统和参数波动系统辨识的结果表明.该方法具有较快的收敛速度     

Active noise control using an adaptive bacterial foraging optimization algorithm

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.     

Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm

The adaptive nonlinear predistorter is an effective technique to compensate for the nonlinear distortion existing in digital communication and control systems. However, available adaptive nonlinear predistorters using indirect learning are sensitive to measurement noise and do not perform optimally. Other available types are either slow to converge, complicated in structure and computationally expensive, or do not consider the memory effects in nonlinear systems such as a high power amplifier (HPA). In this paper, we first propose several novel adaptive nonlinear predistorters based on direct learning algorithms - the nonlinear filtered-x RLS (NFXRLS) algorithm, the nonlinear adjoint LMS (NALMS) algorithm, and the nonlinear adjoint RLS (NARLS) algorithm. Using these new learning algorithms, we design adaptive nonlinear predistorters for an HPA with memory effects or for an HPA following a linear system. Because of the direct learning algorithm, these novel adaptive predistorters outperform nonlinear predistorters that are based on the indirect learning method in the sense of normalized mean square error (NMSE), bit error rate (BER), and spectral regrowth. Moreover, our developed adaptive nonlinear predistorters are computationally efficient and/or converge rapidly when compared to other adaptive nonlinear predistorters that use direct learning, and furthermore can be easily implemented. We further simplify our proposed algorithms by exploring the robustness of our proposed algorithm as well as by examining the statistical properties of what we call the "instantaneous equivalent linear" (IEL) filter. Simulation results confirm the effectiveness of our proposed algorithms     

空间自适应有源噪声控制技术

应用有源噪声控制理论,在比较反馈控制结构和前控制结构后,提出采用模型参考自适应中的ＭＲＡＳ趋稳定性设计方法,构造一种反馈结构自适应降噪的实施方案。实验表明,消声效果明显。     

非线性系统自适应逆模理论及算法

本文在对输入过程作可分性的假设下,采用描述非线性系统的Block-oriented模型导出了自适应非线性系统应满足的广义Wiener-Hopf方程。由此出发得到了一类非线性系统的逆模理论解。类似于LMS算法提出了一种自适应逆模的实用算法。计算机模拟试验证实该算法以极高的精度收敛于理论解。     

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.     

输出不可量测非线性系统的神经模型参考自适应控制

该文针对被控对象输出不可量测的非线性系统,引入一个便于在线辨识的扩展神经网络模型,提出一种基于前馈-反馈结构的神经网络模型参考自适应控制方法。给出了具有全局收敛性的网络训练算法,并分析了控制系统的稳定性。仿真结果表明该控制方法是有效的,而且对网络初始权值的选取及被控对象特性参数的扰动都具有良好的鲁棒性。     

一类非均匀采样非线性系统的无模型自适应控制

对一类未知的非均匀采样离散时间非线性非仿射系统,基于在受控系统当前工作点处的等价数据模型,利用输入输出数据对伪雅可比矩阵（PJM）的在线估计,设计出相应的无模型自适应控制器.所提出的控制方法具有如下特点：控制器的设计仅需要非均匀采样的输入输出数据,不包含系统模型的任何信息;控制算法计算量小,算法容易实现.最后,通过一个非均匀采样离散时间非线性系统仿真结果验证提出方法的有效性.     

A New Approach to Observer-Based Fault-Tolerant Controller Design for Takagi-Sugeno Fuzzy Systems with State Delay

This paper deals with the problem of active fault-tolerant control (FTC) for time-delay Takagi-Sugeno (T-S) fuzzy systems based on a fuzzy adaptive fault diagnosis observer (AFDO). A novel fuzzy fast adaptive fault estimation (FAFE) algorithm for T-S fuzzy models is proposed to enhance the performance of fault estimation, and sufficient conditions for the existence of the fault estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained on-line fault estimation information, an observer-based fast active fault-tolerant controller is designed to compensate for the effect of faults by stabilizing the closed-loop system. Simulation results of a track trail system and a nonlinear numerical example are presented to illustrate the effectiveness of the proposed method.     

A new solution to adaptive control

A new solution to adaptive control for a linear time-variant multivariable process with unknown parameters is presented. The proposed method requires knowledge of only input and output data and, consequently, no state estimation is necessary. The control signal is generated by a control block placed in series with the process. The control block behaves as the "adaptive" inverse of the process, and has as its input the desired output of the process. The whole control system is asymptotically hyperstable. The control system can solve the main problems encountered in process control: structural differences and parameter variations. In addition, it behaves satisfactorily under the influence of perturbations. The method is extremely simple to implement and quite general in scope. Various examples are included for illustration.     

Nonlinear system modeling by competitive learning and adaptivefuzzy inference system

Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First, the algorithm partitions the input space into some local regions by competitive learning, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares (RLS). In the learning algorithm, the key role of the decision boundaries is highly emphasized. To demonstrate the validity of the proposed learning approach and the new adaptive fuzzy inference system, four examples are studied by the proposed method and compared with the previous results