汉字的系统性与词汇的系统性

汉语词汇各自的小系统和系统性与汉语文字各自的小系统和系统性之间,是有一定程度(而非完全彻底)对应关系的;探寻汉语词汇系统性与汉语文字系统性及其对应关系,具有多方面的理论价值和实践意义。有鉴于此,本文探讨了字与词的义类系统性、字与词的同源系统性和字与词的网络系统性。

论网络舆论正负向价值的系统性引导与消解

网络舆论对社会发展既有正向的价值,又有负向的价值。对网络舆论的正向价值,要予以积极引导;对网络舆论的负向价值,要给予尽力消解。针对影响网络舆论正向价值实现的主客体因素和导致网络舆论负向价值实现的主客体因素,采取相应的引导策略和消解策略,就会构建起促进网络舆论正向价值能够得到充分实现而其负向价值被尽力消解的社会系统工程。

SIMULATION AND PERFORMANCE ANALYSIS OF NETWORK ON CHIP ARCHITECTURES

The network on chip（NoC）is used as a solution for the communication problems in a complex system on chip（SoC）design.To further enhance performances,the NoC architectures,a high level modeling and an evaluation method based on OPNET are proposed to analyze their performances on different injection rates and traffic patterns.Simulation results for general NoC in terms of the average latency and the throughput are analyzed and used as a guideline to make appropriate choices for a given application.Finally,a MPEG4 decoder is mapped on different NoC architectures.Results prove the effectiveness of the evaluation method.

Identification of Hammerstein Model Using Hybrid Neural Networks

The identification problem of Hammerstein model with extension to the multi input multi output (MIMO) case is studied. The proposed identification method uses a hybrid neural network (HNN) which consists of a multi layer feed forward neural network (MFNN) in cascade with a linear neural network (LNN). A unified back propagation (BP) algorithm is proposed to estimate the weights and the biases of the MFNN and the LNN simultaneously. Numerical examples are provided to show the efficiency of the proposed method.

Approximation Property of the Modified Elman Network

A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.

Decentralized Control Based on FNNSMC for Interconnected Uncertain Nonlinear Systems

A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large scale systems with unknown bounds of high order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of known bounds of uncertainties and disturbances. The FNNSMC, which incorporates the fuzzy neural networks (FNN) and the SMC, can eliminate the chattering by using the continuous output of the FNN to replace the discontinuous sign term in the SMC. The bounds of uncertainties and disturbances are also not required in the FNNSMC design. The simulation results show that the FNNSMC has more robustness than the SMC.

APPLICATION OF ASSOCIATIVE MEMORY NEURAL NETWORK IN HIGH VOLTAGE TRANSMISSIONLINE FAULT DIAGNOSIS

Effective methods of enhancing the fault-tolerance property are proposed for two kinds of associative memory (AM) neural network (NN) used in high voltage transmission line fault diagnosis. For feedforward NN (FNN),the conception of 'fake attaction region' is presented to expand the attraction region artificially,and for the feedback Hopfield bidirectional AM NN (BAM-NN),the measure to add redundant neurons is taken to enhance NN's memory capacity and fault-tolerance property. Study results show that the NNs built not only can complete fault diagnosis correctly but also have fairly high fault-tolerance ability for disturbed input information sequence. Moreover FNN is a more convenient and effective method of solving the problem of power system fault diagnosis.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

STUDY ON MEASURES OF FAULT TOLERANCE FOR DISTRIBUTED SYSTEM

Fault tolerant ability is an important aspect for overall evaluation of distributed system(DS). This paper discusses three measures for the evaluation: node/edge connectivity, number of spanning trees and synthetic connectivity. A numerical example for illustration and analysis is given, and the synthetic connectivity measure presented by this paper is proved to be rational and satisfactory.

THE MODEL VALIDATION OF DYNAMIC NEURAL NETWORKS

This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.Based on the estimation theory,a synthetic error-index(SEI)criterion for the neural network models has been developed.By using the powerful training algorithm of recursive prediction error (RPE),two simulated non-linear systems are studied,and the results show that the synthetic error-index criterion can be used to verify the dynamic neural network models.Furthermore,the proposed technique is much simple in calculation than that of the effective correlation tests.Finally,some problems required by further study are discussed.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

Consistency and Asymptotic Property of a Weighted Least Squares Method for Networked Control Systems

In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.

Robust model predictive control with randomly occurred networked packet loss in industrial cyber physical systems

For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.

Application of GML and SVG in the development of WebGIS

Our study deals with two major issues impacting current WebGIS development： interoperability of heterogeneous data and visualization of vector data on the Web. By using the combination of Geography Markup Language （GML）, Scalable Vector Graphics （SVG） and Web Feature Service （WFS） Implementation Specifications developed by the OpenGIS Consortium （OGC）, a strategy of WebGIS is proposed. The GML is used as a coding and data transportation mechanism to realize interoperability, the SVG to display GML data on the Web and the WFS as a data query mechanism to access and retrieve data at the feature level in real time on the Web. A case study shows that the combination mentioned above has enormous potential to achieve interoperability while not requiring considerable changes to existing legacy data. Original data formats need not be changed and could still be retrieved using WFS and transformed into GML in real time. SVG can oroduce suoerior ouality vector maps on a Web browser.

Artificial neural network based inverse design method for circular sliding slopes

Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.

Stability analysis and stabilization of wireless networked control systems based on deadband control scheduling

The stability analysis and stabilization problems of the wireless networked control systems(WNCSs) with signal transmission deadbands were considered. The deadbands were respectively set up at the sensor to the controller and the controller to the actor sides in the WNCS, which were used to reduce data transmission, furthermore, to decrease the network collision and node energy consumption. Under the consideration of time-varying delays and signal transmission deadbands, the model for the WNCS was presented. A novel Lyapunov functional which took full advantages of the network factors was exploited. Meanwhile, new stability analysis and stabilization conditions for the WNCS were proposed, which described the relationship of the delay bounds, the transmission deadband bounds and the system stability. Two examples were used to demonstrate the effectiveness of the proposed methods. The results show that the proposed approach can guarantee asymptotical stability of the system and reduce the data transmission effectively.

Modeling and Control of Nonlinear Discrete-time Systems Based on Compound Neural Networks

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.

Coexistence Studies on the Interference Performance Between Mobile Satellite System and LTE Network

The interference coexistence performance between the systems of a Long Term Evolution(LTE) network and Mobile Satellite Service(MSS) system,both of which work around the frequency of approximately 2 GHz according to the bandwidth allocation in China,is proposed in this article.The related background of an MSS system and the common terminologies are introduced.Further,the coexistence scenarios of different coverage situations shared by the two systems are discussed.The interference impact of an MSS transmission on an LTE system is evaluated using a specific Monte Carlo method.Finally,simulation results show that although no crucial issues for the coexistence of an LTE downlink and the MSS are observed,the LTE uplink suffers a considerable amount of interference from the MSS transmission.Given the insufficient isolation between LTE and MSS existing networks,and the rapid advance in the field of LTE,problems may emerge in the near future.Further,this article can be of significance in providing reference for frequency spectrum planning in the existing LTE networks.

Ecological Network Analysis Quantifying the Sustainability of Regional Economies: A Case Study of Guangdong Province in China

To meet the challenge of sustainable development, sustainability must be made. Ecological network analysis(ENA) was introduced in this paper as an approach to quantitatively measure the growth, development, and sustainability of an economic system. The Guangdong economic networks from 1987 to 2010 were analyzed by applying the ENA approach. Firstly, a currency flow network among economic sectors was constructed to represent the Guangdong economic system by adapting the input-output(I-O) table data. Then, the network indicators from the ENA framework involving the total system throughput(TST), average mutual information(AMI), ascendency(A), redundancy(R) and development capacity(C) were calculated. Lastly, the network indicators were analyzed to acquire the overall features of Guangdong's economic operations during 1987–2010. The results are as follows: the trends of the network indicators show that the size of the Guangdong economic network grows exponentially at a high rate during 1987–2010, whereas its efficiency does not present a clear trend over its whole period. The growth is the main characteristic of the Guangdong economy during 1987–2010, with no clear evidence regarding its development. The quantitative results of the network also confirmed that the growth contributed to a great majority of the Guangdong economy during 1987–2010, whereas the development's contribution was tiny during the same period. The average value of the sustainability indicator(α) of the Guangdong economic network was 0.222 during 1987–2010, which is less than the theoretically optimal value of 0.37 for a sustainable human-influenced system. The results suggest that the Guangdong economic system needs a further autocatalysis to improve its efficiency to support the system maintaining a sustainable evolvement.