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.     

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.     

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。     

A Delay-Dependent Approach to Robust H ∞ Control for Uncertain Stochastic Systems with State and Input Delays

In this paper, the problem of delay-dependent robust H _∞ control for uncertain stochastic systems with state and input delays is investigated. The time delays are assumed to be bounded and time varying and the uncertainties are assumed to be norm bounded. By using the Lyapunov functional method, a new delay-dependent robust H _∞ control scheme is presented in terms of linear matrix inequalities (LMIs). Some numerical examples are given to illustrate the effectiveness of the proposed approach. This work is partially supported by the Natural Science Foundation of China (60674055, 60774047), and the Taishan Scholar Programme of Shandong Province. An erratum to this article can be found at     

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。     

基于免疫否定选择算法的机场航班延误状态检测与实现

把免疫算法中的否定选择算法运用到航班延误状态的诊断检测与预测中,将生物免疫系统机制与机场航班运行机制联系起来,建立了相应的自体集合、检测细胞、抗原信息及它们之间的匹配模型,给出了检测方法。首先根据机场航班运行数据生成航班检测的成熟检测器和记忆检测器,然后用否定选择算法和航班数据对检测器进行训练和改进,最后利用检测器中的信息对机场航班状态分段进行预测,获得每个时段的航班延误状况,为机场有关部门对航班延误应急处理提供决策支持。仿真实验表明,该方法能较准确地预测航班的延误状态,且实时性好。     

NEAX61Σ交换机利用现有的资源提高接通率的两种方式

介绍了灵活运用NEAX61Σ交换机现有的软硬件资源 ,修改adc和rlst数据后能使用语音信箱的有关功能 ,进而提高落地接续接通率的两种方法。     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Continuous real-time estimating of compaction quality during the construction of a hot mix asphalt (HMA) pavement is addressed in this paper. The densification of asphalt pavements during construction usually is accomplished by using vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. The measured vibrations of the compactor along with process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the asphalt mat density. Contrary to existing techniques in the literature in which a model is developed to fit experimental data and to predict mat density, a neural network-based approach is adopted that is model-free and uses pattern-recognition techniques to estimate density. The neural network is designed to read the entire frequency spectrum of roller vibrations and to classify these vibrations into different levels. The intelligent asphalt compaction analyzer (IACA) is then trained to convert these vibration levels into a “number” indicative of the asphalt mat density at a given location. This two-step process eliminates the need for regression analysis and produces more accurate density measurements than those reported elsewhere in the literature. Compaction studies of HMA mixes on a stiff subgrade indicate that the changes in the vibration characteristics of the roller are attributable to an increased compaction of the HMA base. The results also show that, with the neural network working as a classifier, the IACA can estimate the density continuously, and in real time, with accuracy levels adequate for quality control in the field.