Arrival first queueing networks with applications in kanban production systems

In this paper, we introduce a new class of queueing networks called arrival first networks. We characterise its transition rates and derive the relationship between arrival rules, linear partial balance equations, and product form stationary distributions. This model is motivated by production systems operating under a kanban protocol. In contrast with the conventional departure first networks, where a transition is initiated by service completion of items at the originating nodes that are subsequently routed to the destination nodes (push system), in an arrival first network a transition is initiated by the destination nodes of the items and subsequently those items are processed at and removed from the originating nodes (pull system). These are similar to the push and pull systems in manufacturing systems.

Our characterisation provides necessary and sufficient conditions for the network to possess linear traffic equations, and sufficient conditions for the network to have a product form stationary distribution. We apply our results to networks operating under a kanban mechanism and characterise the rate at which items are pulled as well as the routing and blocking protocols that give rise to a product form stationary distribution.     

Computer Generated Holograms for Optical Neural Networks

While numerous artificial neural network (ANN) models have been electronically implemented and simulated by conventional computers, optical technology provides a far superior mechanism for the implementation of large-scale ANNs. The properties of light make it an ideal carrier of data signals. With optics, very large and high speed neural network architectures are possible. Because light is a predictable phenomenon, it can be described mathematically and its behavior can be simulated by conventional computers. A hologram is in essence a capture of the light field at a particular moment in time and space. Later, the hologram can be used to reconstruct the three dimensional light field carrying optical data. This makes a hologram an ideal medium for capturing, storing, and transmitting data in optical computers, such as optical neural networks (ONNs). Holograms can be created using conventional methods, but they can also be computer generated. In this paper, we will present an overview of optical neural networks, with emphasis on the holographic neural networks. We will take a look at the mathematical basis of holography in terms of the Fresnel Zone Plate and how it can be utilized in making computer generated holograms (CGHs). Finally, we will present various methods of CGH implementation in a two layer holographic ONN.     

BP神经网络及其在数据分类中的应用

BP神经网络模型是一种典型的前向型神经网络,具有良好的自学习、自适应、联想记忆、并行处理和非线形转换的能力,是目前应用最为广泛的一种神经网络模型。本文介绍了BP神经网络的实现以及其在数据挖掘分类方面的应用。     

摄像机神经网络标定技术

利用人工神经网络来直接学习图像信息与三维信息之间的关系，不需确定摄像机具体的内部参数和外部参数，也不需知道有关模型或参数的先先验知识。在双目视觉的情况下，两摄像机的位置关系不需具体求出，而是隐含在神经网络之中。实验结果表明神经网络方法的标定结果令人满意，并成功地用于机器人曲线跟踪的实验。     

神经网络结构的动态构筑方法

如何决定人工神经网络的适当规模,以往都是通过试探法实现,不但费时,而且无规律可循。文中基于神经网络的基本学习算法,构筑动态网络结构,使之更符合抽取的新的输入、输出特性。讨论了构筑动态神经网络的一种途径。学习是从最简单的网络(无隐含单元)开始,新的单元一步一步补充,直到网络给出一个满意的模拟值。     

Neural network analysis of switchability of microstrip rectangular patch antenna printed on ferrite material

A switchable microstrip rectangular patch antenna printed on ferrite substrate in the X‐band is presented using general artificial neural network (ANN) analysis. The ferrite substrate offers a number of unique radiation characteristics including switchable and polarized radiations from a microstrip antenna with DC magnetic biasing. In such a case, for particular frequency most of the power is converted into magnetostatic waves and little radiates into air. Subsequently, the antenna behaves as switch off, in the sense that it effectively absent as radiator. Both synthesis and analysis are mainly focused on the switchability of antenna. In this work, radial basis function (RBF) networks are used in ANN models. Synthesis is defined as the forward side and then analysis as the reverse side of the problem. Here, the analysis is considered as a final stage of the design procedure, therefore, the parameters of the analysis ANN network are determined by the data obtained reversing the input–output data of the synthesis network. In the RBF network, the spread value was chosen as 0.01, which gives the best accuracy. RBF is tested with 100 sample frequencies but trained only for particular cutoff 15 sample frequencies. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

一种基于函数型神经网络的传感器静态模型辨识方法

本文提出一种基于函数型神经网络的传感器静态模型辨识方法,该神经网络连接系数直接反映了传感器静成模型中的被辨识参数,网络结构简单,具有良好的收敛性,文章将这一方法实际应用到铂热电阻静态模型辨识,仿真结果表明,本方法是可行的。     

Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network

A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification.     

混沌系统的神经网络预测控制

提出一种基于预测控制的神经网络控制方法，将模型未知时的混沌运动控制到不稳定的不动点（UFP）处，该控制系统不需要UFP的位置及其局性态等知识，它包括观测器、带反馈校正的神经网络在预测器和在线训练的神经网络控制器，其方法简便，收敛速度比现有同类方法快得多，同时还分析了控制系统的稳定性，并证明了神经网络控制器的收敛性，理论推导和仿真结果都表明了该方法的有效性。     

Neural networks and their applications in component design data retrieval

Neural networks have gained increased importance in the past few years. One of the basic characteristics of neural networks is the property of associative memory. In this paper we study the possibility of using the ideas of neural networks and associative memory in the manufacturing domain, with specific reference to design data retrieval in group technology. A two-layer feed-forward perceptron with backpropagation is simulated on a Vax-8550 to train example parts. The complete scheme along with the simulation results are explained and future directions indicated.     

人工神经元网络在过程分析和控制中的应用

综述了人工神经元网络方法的过程分析与控制中的应用，包括过程建模、过程控制、模式识别与聚类分析以及在电子鼻当中的应用。     

Towards a framework for multiple artificial neural network topologies validation by means of statistics

Artificial neural networks (ANNs) are flexible computing tools that have been applied to a wide range of domains with a notable level of accuracy. However, there are multiple choices of ANNs classifiers in the literature that produce dissimilar results. As a consequence of this, the selection of this classifier is crucial for the overall performance of the system. In this work, an integral framework is proposed for the optimization of different ANN classifiers based on statistical hypothesis testing. The framework is tested in a real ballistic scenario. The new quality measures introduced, based on the Student t‐test, and employed throughout the framework, ensure the validity of results from a statistical standpoint; they reduce the appearance of experimental errors or the appearance of possible randomness. Results show the relevance of this framework, proving that its application improves the performance and efficiency of multiple classifiers.     

Adaptation of jit phylosophy and kanban technique to a small-sized manufacturing firm; a project management approach

In this study, a project management approach is used, in order to implement some elements of the Just-In-time philosophy to a small-sized manufacturing firm. The objective of this applied research was to overcome some of the problems that most of small firms are facing when implementing JIT, such problems are: lack of materials and human resources, high employees turnover, lack of influence over suppliers, etc. In order to overcome those obstacles, a project management approach was used. As a result of this project, productivity and quality went up, and lead time went down from six to less then two months.     

Experiments of Trust Prediction in Social Networks by Artificial Neural Networks

Social network online services are growing at an exponential pace, both in quantity of users and diversity of services; thus, the evaluation of trust in the interaction among users and toward the system is a central issue from the user point of view. Trust can be grounded in past direct experience or in the indirect information provided by trusted third-party users shaping the trustee reputation. When there is no previous history of interactions, the truster must resort to some form of prediction in order to establish Trust or Distrust on a potential trustee. In this study, we deal with the prediction of trust relationships on the basis of reputation information. Trust can be positive or negative (Distrust), hence, we have a two-class problem. Feature vectors for the classification have binary-valued components. Artificial neural network and statistical classifiers provide state-of-the-art results with these features on a benchmarking trust database. In this article, we propose the application of a sample generation method for the minority class in order to reduce some of the effect of class imbalance among Trust and Distrust classes. Specifically, the approach shows high resiliency to system growth.     

Neural network modelling and control strategies for a pH process

The control of a pH process using neural networks is examined. The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results. In the modelling task, the dynamics of the process was carefully examined to determine a suitable structure for the net. In particular, a multilayer net consisting of two single hidden layers was constructed to reflect the Wiener model of the pH process. This led to much simpler training compared to similar modelling attempts by other researchers. For the control task, two schemes were simulated. In one approach, a net was used to deal with the static nonlinearity to achieve control over a wide working range. The dynamic controller used was the PID, with its parameters tuned on a relay auto-tuner. This control design was compared with the strong acid equivalent method. In the second approach, a direct model reference adaptive neural network control scheme was proposed. The training procedure uses the more efficient least squares algorithm developed by Loh and Fong.     

粗糙集神经网络系统在故障诊断中的应用

故障诊断中的误报和漏报现象直接影响诊断的准确率,同时在线故障诊断又要求很强的实时性,本文在给出粗糙集神经网络系统原理框图的基础上,结合领域知识把该系统应用于滚动轴承的故障诊断中,仿真实验结果表明该系统提高了故障诊断的准确率和诊断速率,同时减少了检测项目,降低了诊断成本,在实际中有良好的应用前景。     

基于人工神经网络铂电阻非线性估计方法的研究􀀁

本文提出了一种基于人工神经网络的铂电阻传感器非线性估计方法,该方法用二次幂级数多项式拟合温度传感器的非线性模型,多项式的系数可由神经网络学习算法得到,当条件发生变化时,只要给出几组测量数据对,通过该方法可自动重新训练网络,获得新的多项式系数,实现传感器的非线性估计。     

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.     

Distributed routing in packet-switching networks by counterpropagation network

Routing is a problem of considerable importance in a packet-switching network, because it allows both optimization of the transmission speeds available and minimization of the time required to deliver information. In classical centralized routing algorithms, each packet reaches its destination along the shortest path, although some network bandwidth is lost through overheads. By contrast, distributed routing algorithms usually limit the overloading of transmission links, but they cannot guarantee optimization of the paths between source and destination nodes on account of the mainly local vision they have of the problem. The aim of the authors is to reconcile the two advantages of classical routing strategies mentioned above through the use of neural networks. The approach proposed here is one in which the routing strategy guarantees the delivery of information along almost optimal paths, but distributes calculation to the various switching nodes. The article assesses the performance of this approach in terms of both routing paths and efficiency in bandwidth use, through comparison with classical approaches.