基于正交小波变换的心冲击图自适应去噪方法

研究了心冲击图的正交小波变换最小均方自适应去噪;阐述了基于正交小波变换的最小均方自适应去噪原理;利用径向高斯核函数对心冲击图进行自适应时频联合分析,得到了中心频率并确定了小波分解尺度;提出了通过选择小波基函数和输入信号长度确定自适应滤波器阶数的方法;从矩阵角度给出了算法的实现步骤,并分析了正交小波变换提高最小均方算法收敛速度的原因.实验结果表明,正交小波变换最小均方算法使自适应去噪后的心冲击图更快达到稳态,随心动周期的变化趋势更加明显.比较去噪前后心冲击图的功率谱密度可知,正交小波变换最小均方算法在保留心冲击图特征的同时自适应地去除了其中的时变噪声,获得了良好的去噪效果.     

基于Chebyshev正交函数神经网络的混沌系统鲁棒自适应同步

提出了基于Chebyshev正交函数神经网络的不确定性混沌系统的鲁棒自适应同步方法.首先,本文提出了正交函数神经网络的网络结构,分析了利用Chebyshev正交多项式形成神经网络的机理.利用Lyapunov稳定性定理确定正交函数神经网络控制器的权值更新规则,并保证权值误差和跟踪误差的有界性.该方法能克服不确定性对混沌系统同步的破坏,实现了良好的同步效果.在本文最后,针对Lorenz系统进行了数值计算,数值计算结果表明了所给方法的有效性.     

基于正交小波变换的自适应语音消噪改进方法

提出一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率．正交小波变换在自适应滤波中起到白化的作用，使自适应滤波器的输入正交化．通过正交小波分解，自适应滤波器中的信号能量降低，输入自相关矩阵的动态范围减小，特征值分布更加集中，从而使收敛速率加快．     

精密直线位移工作台单神经元自适应PID控制方法研究

设计并实现了一套直线位移工作台的精密运动控制系统,建立了直线位移工作台系统各组成部分的数学模型;在分析研究常规PID控制的基础上,针对精密直线位移工作台对定位、响应速度、运动方向等方面的高精度要求,提出了一种适合于高精度直线位移工作台的单神经元自适应PID控制策略,实现了对直线位移工作台的精密运动控制;经实验表明该精密直线位移工作台单神经元自适应PID控制具有良好的动态性能、自适应能力和较强的鲁棒性.     

有观测噪声的时变系统的参数估计

本文给出了有观测噪声、线性离散时变系统的参数估计新方法。它由两段互耦的自适应状态估计器和自适应参数估计器组成。通过引入虚拟时变噪声,我们结合在互耦算法中产生的模型误差到虚拟噪声统计,使模型误差得到有效地补偿和克服滤波发散。模拟例子说明了本文方法的有效性。     

Adaptive array detection in noise and completely unknown jamming

The presence of jamming usually degrades the detection performance of a detector. Moreover, sufficient information about the jamming may be difficult to be obtained. To overcome the problem of adaptive array signal detection in noise and completely unknown jamming, we temporarily assume the jamming belongs to a subspace which is orthogonal to the signal steering vector in the stage of detector design. Consequently, by resorting to the criteria of generalized likelihood ratio test (GLRT) and Wald test, we propose two adaptive detectors, which can achieve signal detection and jamming suppression. It is shown, by Monte Carlo simulations, that the two proposed adaptive detectors have improved detection performance over existing ones.     

Adaptive fuzzy observer backstepping control for a class of uncertain nonlinear systems with unknown time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.     

A fast adaptive model reduction method based on Takenaka-Malmquist systems

An adaptive model reduction method is proposed for linear time-invariant systems based on the continuous-time rational orthogonal basis (Takenaka-Malmquist basis). The method is to find an adaptive approximation in the energy sense by selecting optimal points for the rational orthogonal basis. The stability of the reduced models holds, and the steady-state values of step responses are kept to be equal. Furthermore, the method automatically ensures the reduced system to be in the Hardy space H₂. The existence of the best approximation in the Hardy space H₂ by n Blaschke forms is proved in the proposed approach. The effectivity of this method is illustrated through three well-known examples.     

混合正交遗传算法及其在函数优化上的应用

在分析和研究正交遗传算法的基础之上,依据混合优化策略及混合遗传算法的构造原则,通过对自适应正交局部搜索算子的改进提出了一种新的变异算子。该算子具备自适应全局搜索和局部搜索的能力,能够保证算法的变异概率取值为1.0时,算法的搜索效率最高;结合正交交叉算子之后,又能保证算法的交叉概率也取值为1.0时,算法的搜索效率最高;由此解决了交叉概率和变异概率参数的匹配问题。而使用的截断选择和负相关配对、最优交叉策略、精英选择和重复个体剔除策略等组合算子,一方面能够保证算法的收敛速度;另一方面也能有效地保持种群的多样性,这样在保证算法快速收敛的同时避免出现早熟现象;由此解决了＂全局最优＂和＂快速收敛＂的矛盾。因此,提出的改进型新算法在处理一些常用的测试函数上具有较高的效率。     

A general approach to constructing parameter identification algorithms in the class of square root filters with orthogonal and J-orthogonal tranformations

We study modern implementations of the discrete Kalman filter, namely array square-root algorithms. An important feature of such algorithms is the use of orthogonal and J-orthogonal transformations on each filtering step. For the first time, we develop for this class of algorithms a simple universal approach that lets us generalize any numerically stable implementation of this type to the case of updates in sensitivity equations of the filter with respect to unknown system model parameters. An advantage of the resulting adaptive schemes is their numerical stability with respect to machine rounding errors. Estimation of the noisy state vector of the system and identification of unknown system parameters occur simultaneously. The proposed approach can be used for parameter identification problems, adaptive control problems, experiment planning, and others.     

Reconfiguring wireless phones with adaptive chips

Cellular phone manufacturers are on the horns of a dilemma. On one hand, the demand for their products continues to grow. On the other hand, cellular-phone technology is also changing rapidly. Vendors are dealing with this problem by using an adaptive (also called reconfigurable) chips in a new way. With this approach, software can redraw a chip's physical circuitry on the fly, letting a single processor perform multiple functions. In addition, adaptive computing could increase performance while reducing energy consumption.     

Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.     

一种面向识别的无监督特征学习算法

特征抽取是图像识别的关键环节,准确的特征表达能够产生更准确的分类效果。采用软阈值编码器和正交匹配追踪(OMP)算法正交化视觉词典的方法,以提高单级计算结构的识别率,并进一步构造两级计算结构,获取图像更准确的特征,以提高图像的识别率。实验表明,采用软阈值编码器和OMP算法能提高单级计算结构提取特征的能力,提高大样本数据集中图像的识别率。两级计算结构能够提高自选数据集中图像的识别率。采用OMP算法能提高VOC2012数据中图像的识别率。在自选数据集上,两级计算结构优于单级计算结构,与NIN结构相比表现出优势,与卷积神经网络CNN相当,说明两级计算结构在自选数据集上有很好的适应性。     

Structurally noise resistant classifier for multi-modal person verification

In this letter we propose a piece-wise linear (PL) classifier for use as the decision stage in a two-modal verification system, comprised of a face and a speech expert. The classifier utilizes a fixed decision boundary that has been specifically designed to account for the effects of noisy audio conditions. Experimental results on the VidTIMIT database show that in clean conditions, the proposed classifier is outperformed by a traditional weighted summation decision stage (using both fixed and adaptive weights). Using white Gaussian noise to corrupt the audio data resulted in the PL classifier obtaining better performance than the fixed approach and similar performance to the adaptive approach. Using a more realistic noise type, namely “operations room” noise from the NOISEX-92 corpus, resulted in the PL classifier obtaining better performance than both the fixed and adaptive approaches. The better results in this case stem from the PL classifier not making a direct assumption about the type of noise that causes the mismatch between training and testing conditions (unlike the adaptive approach). Moreover, the PL classifier has the advantage of having a fixed (non-adaptive, thus simpler) structure.     

User authentication via keystroke dynamics based on difference subspace and slope correlation degree

User authentication via keystroke dynamics remains a challenging problem due to the fact that keystroke dynamics pattern cannot be maintained stable over time. This paper describes a novel keystroke dynamics-based user authentication approach. The proposed approach consists of two stages, a training stage and an authentication stage. In the training stage, a set of orthogonal bases and a common feature vector are periodically generated from keystroke features of a legitimate user?s several recent successful authentications. In the authentication stage, the current keystroke feature vector is projected onto the set of orthogonal bases, and the distortion of the feature vector between its projection is obtained. User authentication is implemented by comparing the slope correlation degree of the distortion between the common feature vector with a threshold determined periodically using the recent impostor patterns. Theoretical and experimental results show that the proposed method presents high tolerance to instability of user keystroke patterns and yields better performance in terms of false acceptance rate (FAR) and false rejection rate (FRR) compared with some recent methods.     

拟人机器人关节空间自适应H_∞位置跟踪控制

拟人机器人的建模和控制一直是一种开放的、富于挑战性的问题,研究了一种正交轮式移动车为载体的拟人机器人控制问题。首先建立了系统模型,据此提出一种新的基于NN的自适应H∞位置跟踪控制器,将鲁棒非线性H$控制方法与模型的直接自适应神经网络技术作了自然集成。然后证明了其鲁棒稳定性,并进一步分析了控制器中的重要细节。最后仿真研究验证了该控制器的正确性和有效性。     

Adaptive Kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

In this paper,a composite control scheme for macro-micro dual-drive positioning stage with high acceleration and high precision is proposed.The objective of control is to improve the precision by reducing the influence of system vibration and external noise.The positioning stage is composed of voice coil motor(VCM) as macro driver and piezoelectric actuator(PEA) as micro driver.The precision of the macro drive positioning stage is improved by the combined PID control with adaptive Kalman filter(AKF).AKF is used to compensate VCM vibration(as the virtual noise) and the external noise.The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage.A dynamic recurrent neural networks(DRNN) based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA.Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.     

ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.     

Legendre神经网络非线性信道均衡

在分析Chebyshev正交多项式神经网络非线性滤波器的基础上，利用Legendre正交多项式快速逼近的优良特性以及判决反馈均衡器的结构特点，提出了两种新型结构的非线性均衡器，并利用NLMS算法，推导出自适应算法．仿真表明，无论通信信道是线性还是非线性，Legendre神经网络自适应均衡器与Chebyshev神经网络均衡器的各项性能均接近，而Legendre神经网络判决反馈自适应均衡器能够更有效地消除码间干扰和非线性干扰，误码性能也得到较好的改善．     

Reduced-Order Adaptive Controllers for Fluid Flows Using POD

This article presents a reduced-order adaptive controller design for fluid flows. Frequently, reduced-order models are derived from low-order bases computed by applying proper orthogonal decomposition (POD) on an a priori ensemble of data of the Navier–Stokes model. This reduced-order model is then used to derive a reduced-order controller. The approach discussed here differ from these approaches. It uses an adaptive procedure that improves the reduced-order model by successively updating the ensemble of data. The idea is to begin with an ensemble to form a reduced-order control problem. The resulting control is then applied back to the Navier–Stokes model to generate a new ensemble. This new ensemble then replaces the previous ensemble to derive a new reduced-order model. This iteration is repeated until convergence is achieved. The adaptive reduced-order controllers effectiveness in flow control applications is shown on a recirculation control problem in channel flow using blowing (actuation) on the boundary. Optimal placement for actuators is explored. Numerical implementations and results are provided illustrating the various issues discussed.