基于收敛速度的仿鱼机器人游动力学性能的数值模拟

将速度收敛算法应用于仿鱼机器人直线游动的数值模拟研究中,任意给定初始速度,通过检验游动过程中推进力和阻力的数值动态修正鱼体的游动速度,使其收敛到稳态值,进一步在收敛速度的基础上,对仿鱼机器人稳态游动的力学性能进行数值模拟,揭示其推进机理和流场结构。计算结果表明,改变摆动频率和尾部最大摆幅,采用速度收敛算法可以有效地预测仿鱼机器人的稳态游动速度,进而获得稳态游动的推进力、功率消耗和推进效率,三维流场结构清晰地反映尾部交变的正压梯度与头部的逆压梯度效应。研究结果对于预测仿鱼机器人的稳态游动速度,揭示稳态游动下仿鱼机器人的推进机理,设计仿鱼机器人的运动参数具有重要意义。

混合式遗传算法在机械优化设计中的应用

针对机械优化设计 ,提出了一种混合式遗传算法 (HGA)。该算法和现有的几种MGAs相比 ,不仅保证了全局收敛 ,而且提高了算法收敛速度和稳定性。应用于某水下航行器优化设计的实例 ,显示了该算法的优越性能 ,也展现了遗传算法在机械优化设计中的广阔应用前景。

汽车车内噪声主动控制变步长NFB-LMS算法

为规避最小均方(Least Mean Square,LMS)算法不能同时提高收敛速度和降低稳态误差的固有缺陷,以及已有变步长LMS算法存在收敛速度慢和稳态误差估计精度差的问题,文中提出了一种基于变步长归一化频域块(Normalized Frequency-domain Block,NFB)LMS算法的汽车车内噪声主动控制方法。为了比较,应用传统的LMS算法、基于反正切函数的变步长LMS算法和变步长NFB-LMS算法分别进行实测汽车车内噪声的主动控制。结果表明,与其他两个算法相比,变步长NFB-LMS算法的收敛速度提高了70%以上,稳态误差减小了90%以上。变步长NFB-LMS算法在处理车内噪声信号时具有很高的效率,为进行汽车车内噪声主动控制提供了一种新方法。

汽车车内噪声主动控制迭代变步长LMS算法

针对LMS算法无法同时兼顾收敛速度和稳态误差固有缺陷,及已有变步长LMS算法存在易受噪声干扰影响的问题。文中通过建立步长因子与迭代次数之间的非线性函数关系,提出了一种基于迭代变步长LMS算法的汽车车内噪声主动控制方法。通过将基于LMS算法、变步长LMS算法和迭代变步长LMS算法的汽车车内噪声主动控制结果进行对比,结果表明,与LMS算法相比,迭代变步长LMS算法的收敛速度提高37%;与变步长LMS算法相比,迭代变步长LMS算法的收敛速度提高15%,具有更快的算法收敛速度和较小的稳态误差。

汽车车内降低噪声主动控制泄露LMS算法

泄露变步长最小均方算法是一种改进型LMS算法,克服了LMS算法无法同时兼顾收敛速度和稳态误差的的固有缺陷。提出一种基于泄露变步长LMS算法的汽车车内噪声主动控制方法,并将基于LMS算法和泄露变步长LMS算法的汽车车内噪声主动控制结果进行比较,结果表明:与LMS算法相比,泄露变步长LMS算法具有更快的算法收敛速度和较小的稳态误差,可有效进行汽车车内噪声主动控制。

汽车车内噪声主动控制归一化LMS算法

通过对汽车车内噪声采集试验数据进行分析,运用最小均方(LMS)算法和归一化LMS算法,分别对自适应滤波器中的权向量按照最速下降算法进行更新,并利用建立的自适应滤波器进行汽车车内噪声主动控制。通过比较汽车车内噪声主动控制结果,表明:与LMS算法相比,归一化LMS算法具有更快的算法收敛速度和较小的稳态误差,其收敛速度提高58.3%,其稳态误差降低62.5%。

Contributions to Hom-Schunck optical flow equations-part I： Stability and rate of convergence of classical algorithm

Globally exponential stability （which implies convergence and uniqueness） of their classical iterative algorithm is established using methods of heat equations and energy integral after embedding the discrete iteration into a continuous flow. The stability condition depends explicitly on smoothness of the image sequence, size of image domain, value of the regularization parameter, and finally discretization step. Specifically, as the discretization step approaches to zero, stability holds unconditionally. The analysis also clarifies relations among the iterative algorithm, the original variation formulation and the PDE system. The proper regularity of solution and natural images is briefly surveyed and discussed. Experimental results validate the theoretical claims both on convergence and exponential stability.

Soft Direct-Adaptation Based Bidirectional Turbo Equalization for MIMO Underwater Acoustic Communications

This paper proposes a soft direct-adaptation based bidirectional turbo equalizer for multiple-input multiple-output underwater acoustic communication systems. Soft, rather than hard, direct-adaptation based equalizer combined with the fast self-optimized least mean square algorithm is employed to achieve a faster convergence rate, and the second-order phase-locked loop is embedded into the equalizer to track the time-varying channel. Meanwhile, by utilizing a weighted linear combining scheme, the conventional soft direct-adaptation based equalizer is combined with the time-reversed soft direct-adaptation based equalizer to exploit bidirectional diversity and mitigate error propagation. Both the simulation and experimental results demonstrate that the soft direct-adaptation based bidirectional turbo equalizer outperforms the single-direction soft direct-adaptation based turbo equalizer, and achieves a faster convergence rate than the hard direct-adaptation based bidirectional turbo equalizer.

Construction of LDPC Codes for the Layered Decoding Algorithm

Abstract： The layered decoding algorithm has been widely used in the implementation of Low Density Parity Check （LDPC） decoders, due to its high convergence speed. However, the pipeline operation of the layered decoder may introduce memory access conflicts, which heavily deteriorates the decoder throughput. To essentially deal with the issue of memory access conflicts,

Adaptive swarm-based routing in communication networks

Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.

Incorporate Energy Strategy into Particle Swarm Optimizer Algorithm

The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper. An algorithm is designed which makes all particles originally endowed with a certain level energy, what here we define as EPSO (Energy Strategy PSO). During the iterative process of PSO algorithm, the Inertia Weight is updated according to the calculation of the particle's energy. The portion ratio of the current residual energy to the initial endowed energy is used as the parameter Inertia Weight which aims to update the particles' velocity efficiently. By the simulation in a graph theoritical and a functional optimization problem respectively, it could be easily found that the rate of convergence in EPSO is obviously increased.

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.

时变混合系统的在线FastICA算法

现有的多数盲源分离(BSS,Blind Source Separation)算法都是假设混合系统是时不变的,然而在实际的通信系统中混合系统常常是时变的。传统的快速不动点(FastICA)算法具有快速收敛的优点,但是不能直接用于处理混合系统时变的盲源分离问题。为了提高盲源分离算法的收敛速度和对时变混合系统的跟踪性能,改进了传统FastICA算法,将混合信号分段,在各段样本中估计峭度并采用批处理的方法进行分离。仿真实验表明,改进后的FastICA算法能在时变环境中跟踪混合系统的时变,并能有效地抗多音干扰。

The convergence rates of Shannon sampling learning algorithms

In the present paper,we provide an error bound for the learning rates of the regularized Shannon sampling learning scheme when the hypothesis space is a reproducing kernel Hilbert space(RKHS) derived by a Mercer kernel and a determined net.We show that if the sample is taken according to the determined set,then,the sample error can be bounded by the Mercer matrix with respect to the samples and the determined net.The regularization error may be bounded by the approximation order of the reproducing kernel Hilbert space interpolation operator.The paper is an investigation on a remark provided by Smale and Zhou.

Modified constriction particle swarm optimization algorithm

To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.

SIGNAL ESTIMATION WITH BINARY-VALUED SENSORS

This paper introduces several algorithms for signal estimation using binary-valued outputsensing.The main idea is derived from the empirical measure approach for quantized identification,which has been shown to be convergent and asymptotically efficient when the unknown parametersare constants.Signal estimation under binary-valued observations must take into consideration oftime varying variables.Typical empirical measure based algorithms are modified with exponentialweighting and threshold adaptation to accommodate time-varying natures of the signals.Without anyinformation on signal generators,the authors establish estimation algorithms,interaction between noisereduction by averaging and signal tracking,convergence rates,and asymptotic efficiency.A thresholdadaptation algorithm is introduced.Its convergence and convergence rates are analyzed by using theODE method for stochastic approximation problems.