Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems

In Kim et al. [(1997) A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33(8), 1539–1543], authors present an excellent neural network (NN) observer for a class of nonlinear systems. However, the output error equation in their paper is strictly positive real (SPR) which is restrictive assumption for nonlinear systems. In this note, by introducing a vector b₀ and Lyapunov equation, the observer design is obtained without requiring the SPR condition. Thus, our observer can be applied to a wider class of systems.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

A Recursive Orthogonal Least Squares Algorithm for Training RBF Networks

A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. An illustrative example is given, to demonstrate the effectiveness of the algorithm for eliminating the effects of ill-conditioning in the training data, in an application of neural modelling of a multi-variable chemical process. Comparisons with results from using standard least squares algorithms, in batch and recursive form, show that the ROLS algorithm can significantly improve the neural modelling accuracy. The ROLS algorithm can also be applied to a large data set with much lower requirements on computer memory than the batch OLS algorithm.     

Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

基于支持向量机与神经网络的间谐波测量混合算法

针对目前缺乏国家标准规范间谐波对电能质量的影响,也缺乏有效的手段监测间谐波的问题,用支持向量机理论对其进行了探索。支持向量机理论将间谐波这个非线性问题变换到更高维的空间中进行线性回归,它基于迭代权调整最小二乘法,引入反向传播神经网络算法整定算法参数,通过反馈现场采集、分析信号,并将教师信号与计算信号的差值作为调整参数的驱动信号,在此基础上通过反向遗传算法(BPNN)的反向传播能力对参数进行基于负反馈的调整,提高了算法的收敛速度。通过数值实验对比,该算法比没有使用差动信号的混合算法效率提高约13%。     

无线白组织网中VoIP性能研究

目前,VoIP技术及其业务迅速发展,在无线自组织网络中有广泛的应用,有必要对于VoIP在无线自组网中的传输进行分析研究。利用NS-2作为仿真工具对G.711编码标准下多跳无线自组织网中VoIP的传输进行仿真,使用802．11MAC层协议和AODV路由层协议。实验结果表明,无线自组织网络环境下voIP系统性能（包括丢包率、时延等）受到多跳影响。     

基于地理位置的社交网络信息数据类别分析

移动社交网络的应用已经成为是近几年来应用的热点。现有的基于地理位置的社交网络信息数据有自身的特点,分别论述了基于地理位置的社交网络的信息来源,几种基于地理位置的社交网络所形成的数据集以及其数据类型。     

基于分解技术的动态结构神经网络

本文提出了基于分解技术的动态结构神经网络算法，这种算法能通过分析网络输入输出了空间的维数，确定每一隐含层神经元数目，为了加快学习效率，采用变误差混学习算法，仿真结果验证了这种算法的有效性。     

神经网络智能诊断技术在混凝土结构中的理论实现与展望

混凝土结构诊断技术是现代建筑工业中的一个重要课题。基于人工神经网络的智能诊断,其融合了计算机技术与神经网络技术,并结合了工程设备的特征,它具有非线性、快速、精确等优点,更接近工程实际;同时,具有美好的发展前景。     

Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.