Dynamics of Noisy Neural Nets with Chemical Markers and Gaussian-distributed Connectivities

We have previously investigated the dynamics of probabilistic neural nets with chemical markers and Gaussian distribution of connectivities of the constituent neurons. These investigations have shown that the change from a Poisson to a Gaussian distribution may cause a net to change class. We have now generalized these studies by considering the intrinsic noise of the systems, caused by the spontaneous release of synaptic transmitter substance. A simple mathematical model is developed, the dynamics of which is compared with the Poisson model.     

Combining Neural Network Forecasts on Wavelet-transformed Time Series

We discuss a simple strategy aimed at improving neural network prediction accuracy, based on the combination of predictions at varying resolution levels of the domain under investigation (here: time series). First, a wavelet transform is used to decompose the time series into varying scales of temporal resolution. The latter provides a sensible decomposition of the data so that the underlying temporal structures of the original time series become more tractable. Then, a dynamical recurrent neural netork is trained on each resolution scale with the temporal-recurrent backpropagation algorithm. By virtue of its internal dynamic, this general class of dynamic connections network approximates the underlying law governing each resolution level by a system of non-linear difference equations. The individual wavelet scale forecasts are afterwards recombined to form the current estimate. The predictive ability of this strategy is assessed with the sunspot series.     

Behavioural Aspects of Combining Backpropagation Learning and Self-organizing Maps

Backpropagation learning (BP) is known for its serious limitations in generalizing knowledge from certain types of learning material. In this paper, we describe a new learning algorithm, BP-SOM, which overcomes some of these limitations as is shown by its application to four benchmark tasks. BP-SOM is a combination of a multi-layered feedforward network (MFN) trained with BP and Kohonen's self-organizing maps (SOMs). During the learning process, hidden-unit activations of the MFN are presented as learning vectors to SOMs trained in parallel. The SOM information is used when updating the connection weights of the MFN in addition to standard error backpropagation. The effect of the augmented error signal is that, during learning, clusters of hiddenunit activation patterns of instances associated with the same class tend to become highly similar. In a number of experiments, BP-SOM is shown (i) to improve generalization performance (i.e. avoid overfitting); (ii) to increase the amount of hidden units that can be pruned without loss of generalization performance and (iii) to provide a means for automatic rule extraction from trained networks. The results are compared with results achieved by two other learning algorithms for MFNs: conventional BP and BP augmented with weight decay. From the experiments and the comparisons, we conclude that the hybrid BP-SOM architecture, in which supervised and unsupervised and learning co-operate in finding adequate hidden-layer representations, successfully combines the advantages of supervised and unsupervised learning.     

Modularity,Combining and Artificial Neural Nets

In this paper, the modular combination of artificial neural nets is considered. A modular approach to combining can be contrasted with an ensemble-based approach in that it implies individual modules, each responsible for some specialist aspect of a task, as opposed to each approximating the same function. It is possible to characterize modular systems in terms of (i) reasons for the task decomposition, (ii) the method for accomplishing the decomposition and (iii) the relationship between the modules. These characteristics are considered in brief outlines of the papers in the issue. Reasons for task decomposition include the exploitation of specialist capabilities of individual nets, performance improvement, and making the system easier to understand and modify. Task decomposition may be either automatic (based on the blind application of a data partitioning algorithm) or explicit (based on prior knowledge of the task or the specialist capabilities of the modules), and the relationship between the modules may be successive, cooperative or supervisory.     

基于神经网络算法的轴承外径参数的测量

分析了系统检测轴承外径参数的误差产生原因，提出了神经网络算法来处理检测参数误差的方法。并对其结果进行了分析，取得了满意的结果。     

Speaker Normalization and Model Selection of Combined Neural Networks

This paper introduces bootstrap error estimation for automatic tuning of parameters in combined networks, applied as front-end preprocessors for a speech recognition system based on hidden Markov models. The method is evaluated on a large-vocabulary (10 000 words) continuous speech recognition task. Bootstrap estimates of minimum mean squared error allow selection of speaker normalization models improving recognition performance. The procedure allows a flexible strategy for dealing with inter-speaker variability without requiring an additional validation set. Recognition results are compared for linear, generalized radial basis functions and multi-layer perceptron network architectures.     

BP神经网络在挖掘机液压系统故障诊断中的应用

介绍了BP网络的结构及学习算法,阐明了液压系统故障诊断的实现过程.通过对挖掘机液压系统常见故障进行试验和测试,验证了该神经网络收敛速度快,学习记忆稳定,具有很好的学习功能;测试结果表明该诊断方法具有高可靠性,达到了预期的结果,可以用于液压系统的故障诊断.     

车削加工信息融合的神经网络误差补偿技术

通过对车削加工误差的信息进行综合分析，运用人工神经网络的基本方法，建立了车削加工误差信息融合的神经网络结构的误差补偿模型，从结构和算法上进行了详细阐述；并介绍了在线误差补偿硬件系统的实现方法，以及通过样本的合理选择和系统的学习来提高补偿系统的补偿能力。     

Complex Connectionist Dynamics and the Limited Complexity of the Resulting Functions

The paper demonstrates how algorithmic information theory can be elegantly used as a powerful tool for analyzing the dynamics in connectionist systems. It is shown that simple structures of connectionist systems-even if they are very large-are unable significantly to ease the problem of learning complex functions. Also, the development of new learning algorithms would not essentially change this situation. Lower and upper bounds are given for the number of examples needed to learn complex concepts. The bounds are proved with respect to the notion of probably approximately correct learning. It is proposed to use algorithmic information theory for further studies on network dynamics.     

基于FPA的三关节气动手指的神经网络控制

介绍了一种基于FPA的气动柔性手指,该手指具有体积轻、能直接驱动和柔性好等特点.根据该手指的运动学模型,提出了神经网络控制方法,建立位置控制模块网络(PCNET)和驱动器控制模块网络(ACNET),通过ACNET控制伺服阀输入电压,解决系统延迟问题,通过PCNET解决手指的位置控制.仿真结果表明,该控制算法能很好地实现手指控制的精确度和快速性.     

Hybridization and Specialization of Real-time Recurrent Learning-based Neural Networks

In this article, three different methods for hybridization and specialization of real-time recurrent learning (RTRL)-based neural networks (NNs) are presented. The first approach consists of combining recurrent networks with feedforward networks. The second approach continues with the combination of multiple recurrent NNs. The last approach introduces the combination of connectionist systems with instructionist artificial intelligence techniques. Two examples are added to demonstrate properties and advantages of these techniques. The first example is a process diagnosis task where a hybrid NN is connected to a knowledge-based system. The second example is a NN consisting of different recurrent modules that is used to handle missing sensor data in a process modelling task.     

高精度转台低速特性分析与神经网络预测控制器设计

高精度转台低速特性是转台设计的主要技术指标,低速特性的研究必须建立准确的框架动力学模型并分析出影响低速特性的关键因素,在此基础上采用PID控制器加神经网络模型的预测控制器对转台低速运行时的非线性干扰进行抑制。通过试验证明低速状态下神经网络预测控制器运行的有效性。     

用千兆网搭建企业网网管中心的设计

充分保护和利用现有的网络资源，探讨企业网网络管理中心建设方案的设计以及网络管理、企业网络出口与INTERNET网络设计等，实现千兆网络中心交换机与ATM骨干网中心交换机无缝连接，企业网的统一管理、安全管理及INTERNET接入等。     

Self-organization of Multiple Winner-take-all Neural Networks

In this paper, analysis of the information content of discretely firing neurons in unsupervised neural networks is presented, where information is measured according to the network's ability to reconstruct its input from its output with minimum mean square Euclidean error. It is shown how this type of network can self-organize into multiple winner-take-all subnetworks, each of which tackles only a low-dimensional subspace of the input vector. This is a rudimentary example of a neural network that effectively subdivides a task into manageable subtasks.     

采用神经网络模糊滑模控制的液压驱动活塞位置控制研究

针对液压驱动活塞运动轨迹和压力精度跟踪误差较大问题,设计了神经网络模糊滑模控制,并对控制精度进行仿真验证。创建了液压缸驱动平面简图,推导出液压缸腔室内部参数变化方程式。分析了液压缸驱动压力和位置的变化,采用线性模型建立输入和输出变换方程式。引用滑模控制方法,采用神经网络算法对滑模控制进行逼近,通过模糊切换规则对滑模控制进行自适应调整。采用MATLAB对液压缸活塞轨迹和腔室压力跟踪进行仿真验证,并且与滑模控制输出效果进行比较和分析。结果表明:采用滑模控制方法,液压缸活塞运动轨迹和腔室压力跟踪误差较大;而采用神经网络模糊滑模控制方法,液压缸活塞运动轨迹和腔室压力跟踪误差较小。采用神经网络模糊滑模控制方法,液压缸控制系统自适应调节能力较强,从而提高了活塞运动轨迹和腔室压力跟踪精度。     

浅谈净现值指标

讨论了净现值及其派生指标净现值率、平均盈利指数以及平均年盈利率等指标的概念及特点。通过实例分析了它们在项目经济评价中的优缺点 ,提出了在进行方案比选时 ,上述指标要配合使用 ,才能使项目经济评价的结论更为合理     

Investigations of the redox process of conducting poly(2-methyl-5-amino-1,4-naphthoquinone) (PMANQ) film Interactions of quinone–amine in the polymer matrix

Poly(2-methyl-5-amino-1,4-naphthoquinone) (PMANQ) film is a new functionalized conducting polymer having two distinct redox systems. The polymer structure is of the polyaniline-type, bearing one methylquinone group per monomer unit. The redox mechanisms of PMANQ were investigated by in situ multiple internal reflection Fourier transform infrared spectroscopy (MIRFTIRS) and electrochemical quartz crystal microbalance (EQCM). It is shown that interactions occur between quinone and amine groups in the polymer chain.     

Visual Schemas in Neural Networks for Object Recognition and Scene Analysis

VISOR is a large connectionist system that shows how visual schemas can be learned, represented and used through mechanisms natural to neural networks. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. VISOR is robust against noise and variations in the inputs and parameters. It can indicate the confidence of its analysis, pay attention to important minor differences, and use context to recognize ambiguous objects. Experiments also suggest that the representation and learning are stable, and behavior is consistent with human processes such as priming, perceptual reversal and circular reaction in learning. The schema mechanisms of VISOR can serve as a starting point for building robust high-level vision systems, and perhaps for schema-based motor control and natural language processing systems as well.     

非接触式转轴扭矩测量系统偏心误差的修正研究

轴系运转状态在线监测的主要内容为转轴扭矩的测量。对于基于非接触式转轴扭矩的测量方法,能否精确地测量轴系扭转角度将决定扭矩和功率测量结果的准确性。分析了原系统的偏心误差,以及码盘安装偏心对光电检测器扫过码盘扇形通光孔的时间的影响,并通过几何分析以及运动分解给出了码盘扇形通光孔的通光时间的偏移的具体数学表达式,以此消除测量系统的偏心误差。研究结果对转轴扭矩测量仪的设计提供了理论指导,有利于提高此类设备的测量精度。     

Modular Neural Networks for Medical Prognosis: Quantifying the Benefits of Combining Neural Networks for Survival Prediction

This paper describes a medical application of modular neural networks (NNs) for temporal pattern recognition. In order to increase the reliability of prognostic indices for patients living with the acquired immunodeficiency syndrome (AIDS), survival prediction was performed in a system composed of modular NNS that classified cases according to death in a certain year of follow-up. The output of each NN module corresponded to the probability of survival in a given year. Inputs were the values of demographic, clinical and laboratory variables. The results of the modules were combined to produce survival curves for individuals. The NNs were trained by backpropagation and the results were evaluated in test sets of previously unseen cases. We showed that, for certain combinations of NN modules, the performance of the prognostic index, measured by the area under the receiver operating characteristic curve, was significantly improved (p 0.05). We also used calibration measurements to quantify the benefits of combining NN modules, and show why, when and how NNs should be combined for building prognostic models.