Application of generalized predictive control in networked control system

INTRODUCTION Networked Control System (NCS) is composed of a central controller and a remote system containing a physical plant, sensors and actuators (Halevi and Ray, 1988; Nilsson, 1998; Walsh et al., 1999a; 1999b; Branicky et al., 2000). The controller and the plant are located at different spatial locations and directly connected through network to form a closed loop control. Fig.1 shows the NCS configuration and the time delay induced by network, where u is the control signal an…     

On service differentiation in mobile Ad Hoc networks

A network model is proposed to support service differentiation for mobile Ad Hoc networks by combining a fully distributed admission control approach and the DIFS based differentiation mechanism of IEEE802.11. It can provide different kinds of QoS (Quality of Service) for various applications. Admission controllers determine a committed bandwidth based on the reserved bandwidth of flows and the source utilization of networks. Packets are marked when entering into networks by markers according to the committed rate. By the mark in the packet header, intermediate nodes handle the Received packets in different manners to provide applications with the QoS corresponding to the pre-negotiated profile.Extensive simulation experiments showed that the proposed mechanism can provide QoS guarantee to assured service traffic and increase the channel utilization of networks.     

On service differentiation in mobile Ad Hoc networks

A network model is proposed to support service differentiation for mobile Ad Hoc networks by combining a fully distributed admission control approach and the DIFS based differentiation mechanism of IEEE802.11. It can provide different kinds of QoS (Quality of Service) for various applications. Admission controllers determine a committed bandwidth based on the reserved bandwidth of flows and the source utilization of networks. Packets are marked when entering into networks by markers according to the committed rate. By the mark in the packet header, intermediate nodes handle the received packets in different manners to provide applications with the QoS corresponding to the pre-negotiated profile. Extensive simulation experiments showed that the proposed mechanism can provide QoS guarantee to assured service traffic and increase the channel utilization of networks.     

一种基于双网络模型的图像识别新方法

针对基于复杂网络图像识别方法建模复杂度过高问题,提出一种基于双网络模型的灰度图像识别新方法。首先将像素点作为复杂网络节点,基于灰度乘积构建图像的结构平衡网络模型以及基于欧氏距离构建复杂网络模型,然后分别计算两种网络模型的拓扑特征参量,形成最终的图像特征识别参量。相比现有基于复杂网络的图像识别方法,该方法在理论上能够降低图像建模复杂度,提高图像识别速度。使用 YALE 人脸数据库进行仿真对比实验,结果表明,该方法的图像识别速度为传统复杂网络方法的 35%,正确率提高了 4%。     

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These co…     

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.     

基于择优随机游动的加权网络模型

基于择优随机游动提出一个加权网络模型,并对网络的强度分布、度分布以及权重分布进行分析,结果表明：该模型生成的网络具有无标度特性．     

Multiple objective particle swarm optimization technique for economic load dispatch

INTRODUCTION The conventional economic load dispatch prob-lem of power generation involves allocation of power generation to different thermal units to minimize the operating cost subject to diverse equality and ine-quality constraints of the power system. This makes the economic load dispatch problem a large-scale highly non-linear constrained optimization problem. However, as a result of public awareness of envi-ronmental protection, diverse emission compliance strategies have emerged (…     

Monotone routing in multirate rearrangeable log_d (N,m,p) network

INTRODUCTION Switching networks have been widely used in telecommunication, data communication, satellitecommunication, optical fiber network, etc. It was firstproposed to meet the need to interconnect pairs oftelephone users. When a telephone user wants to callanother one, a request is generated. In order to satisfy all the requests, people first try to fully connect the callers (inputs) and receivers (outputs) who want to communicate. Later Clos (1953) showed that through some clever …     

Exponential synchronization of general chaotic delayed neural networks via hybrid feedback

This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks （CNNs）, bidirectional associative memory （BAM） networks, recurrent multilayer perceptrons （RMLPs）. By virtue of Lyapunov- Krasovskii stability theory and linear matrix inequality （LMI） technique, some exponential synchronization criteria are derived. Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws.     

Calculation method of ship collision force on bridge using artificial neural network

Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method （FEM） which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network （ANN）. The radial basis function neural network （RBFNN） employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation effi- ciency in application using MATLAB software.     

基于NARMAX模型的Hopfield网络系统辨识

1 Introduction Identification of an unknown systemfrom availabledata is i mportant in many fields . There are severalmodels representing systems that are dominated bynonlinear characteristics . NARMAX model provides aunified and si mple representation for a wide class ofdiscrete-ti me nonlinear stochastic systems , proposedby Leontaritis ,et al.[1]System identification usingNARMAX model consists of two stages : model struc-ture determination and parameter esti mation proce-dure . The ide…     

Multiwavelets domain singular value features for image texture classification

A new approach based on multiwavelets transformation and singular value decomposition (SVD) is proposed for the classification of image textures. Lower singular values are truncated based on its energy distribution to classify the textures in the presence of additive white Gaussian noise (AWGN). The proposed approach extracts features such as energy, entropy, local homogeneity and max-min ratio from the selected singular values of multiwavelets transformation coefficients of image textures. The classification was carried out using probabilistic neural network (PNN). Performance of the proposed approach was compared with conventional wavelet domain gray level co-occurrence matrix (GLCM) based features, discrete multiwavelets transformation energy based approach, and HMM based approach. Experimental results showed the superiority of the proposed algorithms when compared with existing algorithms.     

基于无标度网络的异质化 WSNs 抗毁性研究

无线传感器网络（WSNs）由于受到资源与能量限制,能效与抗毁性成为 WSNs 拓扑设计的关键问题。借助无标度网络拓扑演化构建 WSNs 网络拓扑模型,提高网络的能效与抗毁性,从而最终延长 WSNs 的生存周期。根据现有模型,提出一种基于 BA 无标度网络的 WSNs 异质化演化模型,该模型具有 BA 无标度网络良好的随机抗毁性,同时拥有较好的蓄意攻击抗毁性。利用 NetworkX 仿真软件进行对比仿真实验,结果表明,该网络模型在蓄意失效部分节点时,相较于 EAEM 模型,基本无多余节点失效,且网络生命周期提升了 10%左右。     

Graph rigidity and localization of multi-robot formations

This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based on distributed sensor networks and graph rigidity are proposed. A method for estimating the quality of localizations via a linearized weighted least-squares algorithm is presented, which considers incomplete and noisy sensory information. The approach in this paper had been implemented in a multi-robot system of five car-like robots equipped with omni-directional cameras and IEEE 802.11b wireless network.     

Graph rigidity and localization of multi-robot formations

INTRODUCTION In order for a team of mobile robots to navigateautonomously in some desired formations and fur-ther perform cooperative tasks, such as surveillanceand target acquisition, they must be able to localizethemselves in the formation as well as in a globalreference frame (Belta and Kumar, 2001; Das et al.,2002). Therefore, how to estimate the robots’po-sitions and orientations (poses) in a precise andefficient way is of particular interest. Our interestwas in localizing a team o…     

A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

INTRODUCTION The reactive power dispatch is aimed at mini- mizing the active power loss in the transmission network by allocating the reactive power generation under several security constraints. The reactive power dispatch problem has significant influence on secure and economic operation of power systems. The reac- tive power generation affects the overall generation cost via transmission loss. A procedure which allo- cates the reactive power generation so as to minimize the transmissio…     

Social media networks and pedagogy at the University of Jordan

This study examined impact of a social media networks course on student use of SNSs performance. Moreover, it examined the associations among course design, course materials, learning experiences and a social media networks course. Survey instrument is used to examine the relationships in the proposed model. A total of 380 questionnaires have been collected from students at the University of Jordan who studied the social media networks course. A structural equation modelling approach based on AMOS 20.0 statistical software is used to study the causal relationships and test the hypotheses between the observed and latent constructs in the proposed research model. The analysis results revealed that course materials and learning experiences directly, positively and significantly impacted the social media networks course, which in turn had a significant impact on students’ use social networks sites performance. Course design, however, did not impact the social media networks course. Our findings have important implications as we demonstrated the validity of the joint two different models and provide information about impact of studying social media networks course on students’ academic performance.     

波长路由在光互连计算机网络中的应用

把波分复用技术应用于计算机光互连网络 ,通过分析波长和网络路由之间的关系 ,提出了采用波长作为 IP寻径“网络地址”,实现网络间路由寻径的新概念 ,使网络具有非常好的可扩展性 .介绍了实验室利用双波长环网构成机群系统验证波长路由的工作进展及关键单元设计 .性能测试表明 ,光互连技术的应用 ,是解决网络带宽不足的理想技术 .     

Nonlinear modelling of a SOFC stack by improved neural networks identification

The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.