时间分割的池计算网络及其动力学

为解决针对给定任务构建合适的神经元池问题,提出了一种时间分割的神经元池设计方法,该方法将多个子神经元池顺序连接,每两个相邻的子神经元池之间嵌入一个滞后环节以构成时间分割的神经元池,每个子神经元池只需处理一段时间的信息,从而达到复杂记忆任务分解的目的.输出层可对各子神经元池的状态进行整合以获取不同时段的输入特征.对多阶层振荡器的实验表明,在宏观参数相同的情况下,时间分割的池计算网络比常规池计算网络具有更强的记忆能力,能够产生更加多样化的动力学行为.     

Resource allocation in grid computing

Grid computing, in which a network of computers is integrated to create a very fast virtual computer, is becoming ever more prevalent. Examples include the TeraGrid and Planet-lab.org, as well as applications on the existing Internet that take advantage of unused computing and storage capacity of idle desktop machines, such as Kazaa, SETI@home, Climateprediction.net, and Einstein@home. Grid computing permits a network of computers to act as a very fast virtual computer. With many alternative computers available, each with varying extra capacity, and each of which may connect or disconnect from the grid at any time, it may make sense to send the same task to more than one computer. The application can then use the output of whichever computer finishes the task first. Thus, the important issue of the dynamic assignment of tasks to individual computers is complicated in grid computing by the option of assigning multiple copies of the same task to different computers. We show that under fairly mild and often reasonable conditions, maximizing task replication stochastically maximizes the number of task completions by any time. That is, it is better to do the same task on as many computers as possible, rather than assigning different tasks to individual computers. We show maximal task replication is optimal when tasks have identical size and processing times have a NWU (New Worse than Used; defined later) distribution. Computers may be heterogeneous and their speeds may vary randomly, as is the case in grid computing environments. We also show that maximal task replication, along with a c μ rule, stochastically maximizes the successful task completion process when task processing times are exponential and depend on both the task and computer, and tasks have different probabilities of completing successfully.     

单输出系统最优自校正滤波新方法及其在跟踪系统中的应用

对于带未知噪声统计且含未知模型参数的单输出系统,本文用现代时间序列分析方法提出了一种新的自校正滤波方法,给出了具有渐近最优性的自校正滤波器,新方法的特点是基于ARMA新息模型通过计算自校正输出预报器和自校正观测噪声滤波器就可得到自校正状态滤波器,文中给出了在跟踪系统中的应用例子,仿真结果说明了新方法的有效性。     

具有双储层结构的动态误差补偿回声状态网络

针对传统回声状态网络难以有效应对高阶非线性复杂模型问题,本文在理论分析的基础上提出了一种双储层结构的误差补偿回声状态网络,并设计了该网络的学习算法.该网络由计算层和补偿层构成,计算层主要承担拟合任务,补偿层则作为状态跟随器,实时补偿由于计算层对期望方差估计不足而导致的幅值偏差.对多阶振荡器和真实高阶非线性数据集的实验结果表明,本文所提网络结构较常规网络具有更高的稳定性和泛化性能,尤其对高阶非线性复杂模型的预测精度大幅度提升.     

基于深度学习的仿生集群运动智能控制

宽度学习系统(BLS)是一种基于RVFLN的高效增量学习系统,具有快速且精度高的特点.为了实现BLS对时间序列的精确预测,结合回声状态网络(ESN)的储备池结构,提出一种基于池计算的宽度学习系统(RCBLS).该系统通过在强化层引入简单环型储备池连接,以并行的储备池代替原系统中的前馈连接,使RCBLS具有一定的回声状态特性且方便设计.同时,应用增量学习保证了系统的实时性能.基于MSO时间序列预测问题,针对不同规模数据样本分别研究不同储备池结构RCBLS的性能.结果表明:多储备池结构的RCBLS大大提高了模型的泛化能力和稳定性.     

多输出组合逻辑系统中软故障的分析

本文运用两变量沃尔什-阿达马变换分析了一般多输出组合逻辑系统因软故障引起的系 统错误概率,导出了计算某些特殊情况及常用的SUM-OF-PRODUCT和NAND-NAND系统错 误概率的简化公式和快速算法.利用计算机研究了几个实例,它们揭示了由软故障引起的系 统错误概率的特征,为设计多输入多输出组合逻辑系统,降低软故障对系统的影响,提供了有 益的启示.     

Time Series Classification in Reservoir- and Model-Space

We evaluate two approaches for time series classification based on reservoir computing. In the first, classical approach, time series are represented by reservoir activations. In the second approach, on top of the reservoir activations, a predictive model in the form of a readout for one-step-ahead-prediction is trained for each time series. This learning step lifts the reservoir features to a more sophisticated model space. Classification is then based on the predictive model parameters describing each time series. We provide an in-depth analysis on time series classification in reservoir- and model-space. The approaches are evaluated on 43 univariate and 18 multivariate time series. The results show that representing multivariate time series in the model space leads to lower classification errors compared to using the reservoir activations directly as features. The classification accuracy on the univariate datasets can be improved by combining reservoir- and model-space.     

基于KNN与矩阵变换的图节点嵌入归纳式学习算法

图节点的低维嵌入在各种预测任务中是非常有用的,如蛋白质功能预测、内容推荐等。然而,多数方法不能自然推广到不可见节点。图采样聚合算法(Graph Sample and Aggregate,Graphsage)虽然可以提高不可见节点生成嵌入的速度,但容易引入噪声数据,且生成的节点嵌入的表示能力不高。为此,文中提出了一种基于KNN与矩阵变换的图节点嵌入归纳式学习算法。首先,通过KNN选取K个邻节点;然后,根据聚合函数生成聚合信息;最后,利用矩阵变换与全连接层对聚合信息和节点信息进行计算,得到新的节点嵌入。为了有效权衡计算时间与性能,文中提出一种新的聚合函数,对邻节点特征运用最大池化作为聚合信息输出,以更多地保留邻节点信息,降低计算代价。在reddit和PPI两个数据集上的实验表明,所提算法在micro-f1和macro-f1两个评价指标上分别获得了4.995%与10.515%的提升。因此,该算法可以大幅减少噪声数据,提高节点嵌入的表示能力,快速有效地为不可见节点及不可见图生成节点嵌入。     

Carrot and stick 2.0: The benefits of natural and motivational prosody in computer-assisted learning

For acquiring new skills or knowledge, contemporary learners frequently rely on the help of educational technologies supplementing human teachers as a learning aid. In the interaction with such systems, speech-based communication between the human user and the technical system has increasingly gained importance. Since spoken computer output can take on a variety of forms depending on the method of speech generation and the employment of prosodic modulations, the effects of such auditory variations on the user’s learning achievement require systematic investigation. The experiment reported here examined the specific effects of speech generation method and prosody of spoken system feedback in a computer-supported learning environment, and may serve as validational tool for future investigations of spoken computer feedback effects on learning. Learning performance in a basic cognitive task was compared between users receiving pre-recorded, naturally spoken system feedback with neutral prosody, pre-recorded feedback with motivating (praising or blaming) prosody, or computer-synthesized feedback. The observed results provide empirical evidence that users of technical tutoring systems benefit from pre-recorded, naturally spoken feedback, and do even more so from feedback with motivational prosodic modulations matching their performance success. Theoretical implications and considerations for future implementations of spoken feedback in computer-based educational systems are discussed.     

Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified Differential Harmony Search Technique

This paper proposes a new Self Evolving Recurrent Neuro-Fuzzy Inference System (SERNFIS) for efficient prediction of highly fluctuating and irregular financial time series data like stock market indices over varying time frames. The network is modeled including the first order Takagi Sugeno Kang (TSK) type fuzzy if then rules with two types of feedback loops. The recurrent structure in the proposed model comes from locally feeding the firing strength of the fuzzy rule back to itself and by including a few time delay components at the output layer. The novelty of the model is based on the fact that the internal temporal feedback loops and time delayed output feedback loops are used for further enhancing the prediction capability of traditional neuro-fuzzy system in handling more dynamic financial time series data. Another recurrent functional link artificial neural network (RCEFLANN) model is also presented for a comparative study. In the second part of the paper a modified differential harmony search (MDHS) technique is proposed for estimating the parameters of the model including the antecedent, consequent and feedback loop parameters. Experimental results obtained by implementing the model on two different stock market indices demonstrate the effectiveness of the proposed model compared to existing models for stock price prediction.     

Improving reservoirs using intrinsic plasticity

The benefits of using intrinsic plasticity (IP), an unsupervised, local, biologically inspired adaptation rule that tunes the probability density of a neuron's output towards an exponential distribution—thereby realizing an information maximization—have already been demonstrated. In this work, we extend the ideas of this adaptation method to a more commonly used non-linearity and a Gaussian output distribution. After deriving the learning rules, we show the effects of the bounded output of the transfer function on the moments of the actual output distribution. This allows us to show that the rule converges to the expected distributions, even in random recurrent networks. The IP rule is evaluated in a reservoir computing setting, which is a temporal processing technique which uses random, untrained recurrent networks as excitable media, where the network's state is fed to a linear regressor used to calculate the desired output. We present an experimental comparison of the different IP rules on three benchmark tasks with different characteristics. Furthermore, we show that this unsupervised reservoir adaptation is able to adapt networks with very constrained topologies, such as a 1D lattice which generally shows quite unsuitable dynamic behavior, to a reservoir that can be used to solve complex tasks. We clearly demonstrate that IP is able to make reservoir computing more robust: the internal dynamics can autonomously tune themselves—irrespective of initial weights or input scaling—to the dynamic regime which is optimal for a given task.     

A reservoir radial-basis function neural network in prediction tasks

A reservoir radial-basis function neural network, which is based on the ideas of reservoir computing and neural networks and designated for solving extrapolation tasks of nonlinear non-stationary stochastic and chaotic time series under conditions of a short learning sample, is proposed in the paper. The network is built with the help of a radial-basis function neural network with an input layer, which is organized in a special manner and a kernel membership function. The proposed system provides high approximation quality in terms of a mean squared error and a high convergence speed using the second-order learning procedure. A software product that implements the proposed neural network has been developed. A number of experiments have been held in order to research the system’s properties. Experimental results prove the fact that the developed architecture can be used in Data Mining tasks and the fact that the proposed neural network has a higher accuracy compared to traditional forecasting neural systems.     

Load Balancing in a Cluster-Based Web Server for Multimedia Applications

We consider a cluster-based multimedia Web server that dynamically generates video units to satisfy the bit rate and bandwidth requirements of a variety of clients. The media server partitions the job into several tasks and schedules them on the backend computing nodes for processing. For stream-based applications, the main design criteria of the scheduling are to minimize the total processing time and maintain the order of media units for each outgoing stream. In this paper, we first design, implement, and evaluate three scheduling algorithms, first fit (FF), stream-based mapping (SM), and adaptive load sharing (ALS), for multimedia transcoding in a cluster environment. We determined that it is necessary to predict the CPU load for each multimedia task and schedule them accordingly due to the variability of the individual jobs/tasks. We, therefore, propose an online prediction algorithm that can dynamically predict the processing time per individual task (media unit). We then propose two new load scheduling algorithms, namely, prediction-based least load first (P-LLF) and prediction-based adaptive partitioning (P-AP), which can use prediction to improve the performance. The performance of the system is evaluated in terms of system throughput, out-of-order rate of outgoing media streams, and load balancing overhead through real measurements using a cluster of computers. The performance of the new load balancing algorithms is compared with all other load balancing schemes to show that P-AP greatly reduces the delay jitter and achieves high throughput for a variety of workloads in a heterogeneous cluster. It strikes a good balance between the throughput and output order of the processed media units     

The effects of multimodal feedback on older adults' task performance given varying levels of computer experience

This experiment examines the effect that computer experience and various combinations of feedback (auditory, haptic, and/or visual) have on the performance of older adults completing a drag-and-drop task on a computer. Participants were divided into three computer experience groups, based on their frequency of use and breadth of computer knowledge. Each participant completed a series of drag-and-drop tasks under each of seven feedback conditions (three unimodal, three bimodal, one trimodal). Performance was assessed using measures of efficiency and accuracy. Experienced users responded well to all multimodal feedback while users without experience responded well to auditory-haptic bimodal, but poorly to haptic-visual bimodal feedback. Based on performance benefits for older adults seen in this experiment, future research should extend investigations to effectively integrate multimodal feedback into GUI interfaces in order to improve usability for this growing and diverse user group.     

一种线性自抗扰控制器的无人直升机姿态控制方法研究

针对无人直升机模型复杂,控制器难于设计,易受外界干扰等问题,本文在建立亚拓系列直升机动力学模型基础上,提出了一种改进的二阶线性自抗扰控制器。首先,将线性扩张状态观测器用于估计影响输出结果的扰动,并加入跟踪微分器。然后,改进了控制器结构与反馈补偿系数,使直升机姿态角能够更快地响应所输入的指令,并能够按设定的角度飞行,以完成要求的任务;最后,通过引入白噪声干扰模块,来验证本文控制器的抗干扰能力。对比仿真结果表明,本文所提出的控制器对于无人直升机的姿态角有较好的控制效果,优于其他两种控制器。特别是在噪声干扰的条件下,也有较好的动态性能和鲁棒性。     

实时控制任务的模糊反馈调度系统研究

分析了实时控制任务的控制性能在不同控制阶段与处理器利用率需求间的关系,提出一种实时控制任务的模糊反馈调度系统.模糊控制器通过监测实时控制任务的误差及其变化率,查询模糊决策表,动态决定任务的优先级,反馈调度器根据优先级分配任务的利用率.仿真结果表明，在计算资源有限时，该方法能有效改善实时控制任务的控制性能.     

Hybrid serial-parallel digital computation

The design of a fully parallel-digital computer broadly equivalent to an analogue computer is outlined. Comparable performance with an analogue is shown from solutions to an optimal control problem involving linear programming. Parallel operation with a serial digital computer carrying out dynamic programming, demonstrates the performance feasibility of the parallel digital replacing the analogue computer and providing an all digital hybrid serial-parallel computing facility or on-line computer controller.     

An approach to reservoir computing design and training

Reservoir computing is a framework for computation like a recurrent neural network that allows for the black box modeling of dynamical systems. In contrast to other recurrent neural network approaches, reservoir computing does not train the input and internal weights of the network, only the readout is trained. However it is necessary to adjust parameters to create a “good” reservoir for a given application. In this study we introduce a method, called RCDESIGN (reservoir computing and design training). RCDESIGN combines an evolutionary algorithm with reservoir computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir matrix by the spectral radius. The idea of adjust the spectral radius within the unit circle in the complex plane comes from the linear system theory. However, this argument does not necessarily apply to nonlinear systems, which is the case of reservoir computing. The results obtained with the proposed method are compared with results obtained by a genetic algorithm search for global parameters generation of reservoir computing. Four time series were used to validate RCDESIGN.     

Algorithm,software, and hardware optimizations for Delaunay mesh generation on simultaneous multithreaded architectures

This article focuses on the optimization of PCDM, a parallel, two-dimensional (2D) Delaunay mesh generation application, and its interaction with parallel architectures based on simultaneous multithreading (SMT) processors. We first present the step-by-step effect of a series of optimizations on performance. These optimizations improve the performance of PCDM by up to a factor of six. They target issues that very often limit the performance of scientific computing codes. We then evaluate the interaction of PCDM with a real SMT-based SMP system, using both high-level metrics, such as execution time, and low-level information from hardware performance counters.