基于速率的反馈拥塞控制的最优化问题研究

基于速率的反馈拥塞控制用于ＡＴＭ网络中可用位速率（ＡＢＲ）流量控制。可用位速率业务需要最优和稳定的流量控制器来保证高的吞吐量和保证网络的稳定性。本文采用线性控制理论来设计基于速率的流量控制器。这个控制器是一个简单的比例控制器,使用品质因数来优化参数。文中证实了最优化控制器对系统参数的最小依赖性。     

基于T—S模糊神经网络的ATM网络业务量智能控制

ATM网络的业务量控制是ATM网络中的关键技术之一。在充分考虑了模糊神经网络的学习功能后,提出了利用T－S模糊神经网络算法对ATM网络进行呼叫接纳控制。仿真结果表明,该方法提高了网络对呼叫的实时处理能力,又增加了网络资源的利用率。     

Connection admission control of ATM network using integrated MLP and fuzzy controllers

This paper presents a new approach to the problem of call admission control (CAC) of variable bit rate (VBR) traffic in an asynchronous transfer mode (ATM) network. Our approach employs an integrated neural network and fuzzy controller to implement the CAC controller. This scheme capitalizes on the learning ability of a neural network and the robustness of a fuzzy controller. Experiments show that this scheme is able to achieve high throughput and low cell loss while achieving fairness among different classes of VBR traffic. For comparison, we have also implemented four other CAC schemes: (1) peak bandwidth method, (2) equivalent bandwidth method, (3) average bandwidth method and (4) neural network quality of service (QoS) predictor. Results of these experiments are presented in this paper.     

一种基于FNN的高速网络拥塞控制策略

以ATM（asynchronous transfer mode)为研究对旬,同种基于模糊神经网络（fuzzy neural network,简称FNN）的流量预测和拥塞控制策略,拥塞控制是高速网络（如ATM）研究中的关键问题之一,传统的基于BP神经网络的流量预测方法因其收敛速度较慢且具有较大的误差,影响了拥塞控制效果,而模糊神经网络由于具有处理不确定性问题和很强的学习能力,很好地解决这一问题,最后通过仿真,比较和分析了基于BP神经网络和基于FNN方法和性能,证明此方法是有效的。     

遗传算法与神经网络在接纳控制中的应用

ATM网络的业务量控制是ATM网络中的关键技术之一。连接接纳控制是业务量控制的一种,对业务源进行控制。该文采用遗传算法和神经网络对ATM网络进行连接接纳控制,是一种比较可行的方法。     

An adaptive scheme for admission control in ATM networks

This paper presents a real time front-end admission control scheme for ATM networks. A call management scheme which uses the burstiness associated with traffic sources in a heterogeneous ATM environment to effect dynamic assignment of bandwidth is presented. In the proposed scheme, call acceptance is based on an on-line evaluation of the upper bound on cell loss probability which is derived from the estimated distribution of the number of calls arriving. Using this scheme, the negotiated quality of service will be assured when there is no estimation error. The control mechanism is effective when the number of calls is large, and tolerates loose bandwidth enforcement and loose policing control. The proposed approach is very effective in the connection oriented transport of ATM networks where the decision to admit new traffic is based on the a priori knowledge of the state of the route taken by the traffic.     

基于模糊神经网络的ATM网络业务量智能预测

文章尝试将模糊神经网络方法引入ATM网络的业务量预测中。ATM网络业务源一般是随机产生的时变信号,其模型一般很难描述。文章充分考虑了模糊神经网络的学习功能,通过对相关模型的仿真,能够很好地描述ATM网络中的业务流特性,对多媒体的业务量做出了准确的预测。与传统的神经网络方法比较,具有更好的逼近效果。     

A neural network controller for congestion control in ATM multiplexers

This paper presents and adaptive approach to the problem of congestion control arising at the User-to Network Interface (UNI) of an ATM multiplexer. We view the ATM multiplexer as a non-linear stochastic system whose dynamics are ill-defined. Real-time measurements of the arrival rate process and the queueing process, are used to identify, and minimize congestion episodes. The performance of the system is evaluated using a performance-index function which is a quantative measure of “how well” the system is performing. A three-layers backpropagation neural network controller generates a signal that attempts to minimize congestion without degrading the quality of the traffic. During periods of buffer over-load the control signal, adaptively, modulates the arrival process such that its peak-rate is throttled-down. As soon as congestion is terminated, the control signal is adjusted such that the coding rates are restored back to their original values. Adaptability is achieved by continuously adjusting the weights of the neural network controller such that the performance of the system, measured by its performance index function, is maximized over a certain optimization period. The performance index function is defined in terms of two main objectives: (1) to minimize the cell loss rate (CLR), i.e., minimize congestion episodes, and (2) to maintain the quality of the video/audio traffic by maintaining its original source coding rate. The neural network learning process can be viewed as a specialized form of reinforcement learning in the sense that the control signal is reinforced if it tends to maximize the performance index function. Performance evaluation results prove that this approach is effective in controlling congestion while maintaining the quality of the traffic.     

Buffer allocation optimization in ATM switching networks using ALOPEX algorithm

We describe the use of a stochastic algorithm, called ALOPEX, which could be implemented in VLSI for optimizing the buffer allocation process in ATM switching networks. We present the results of computer simulations for buffer allocation in ATM switching networks using the ALOPEX algorithm. The algorithm uses a scalar cost function which is a measure of global performance. The ALOPEX works by broadcasting the global cost function to all neural processors in the neural network. Since each neural processor solely depends on the global cost function no interaction is needed between the neural processors and the algorithm is more amenable to massively parallel implementation. The application of the ALOPEX algorithm for the buffer allocation optimization in ATM networks assumes limited buffer capacity. The proposed ALOPEX-based approach takes advantage of the favorable control characteristics of the algorithm such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically.     

ATM网络传输控制机制研究及进展

ATM网络中的传输控制方法的研究涉及到网络中的服务质量、服务类型。已经提出了许多不同特点的控制机制,主要集中在基于许可证方案和基于速率方案的设计,随着ATM广域网应用和因特网信息浏览的增多,新的研究热点是有速度反馈控制的用于ABR服务类型的传输控制技术。本文进一步研究和讨论ATM网络中所采用拥塞控制策略存在的问题和在当今网络应用考虑的主要因素。     

Predictive congestion control of ATM networks: multiple sources/single buffer scenario

This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency.We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.     

一种径向基函数神经网络预测在超临界温度控制系统中的应用研究

超临界温度控制系统具有较大的惯性、时滞和非线性,且动态特性随运行工况而改变,难以建立其精确的数学模型,本文采用GGAP算法的RBF神经网络构成神经网络预测控制器,将在线学习和预测控制相结合,以某超临界电厂主汽温度为研究对象,MATLAB仿真实验表明,该方法能对超临界温度控制系统实现有效的控制,动态性能较传统的PID控制有较大的提高。     

城市单路口两级加权神经网络控制系统设计

城市交通系统是一个非常复杂的非线性系统,很难建立精确的数学模型,而BP神经网络具有较强的自学习、自适应的特点,适合复杂的大系统。针对单交叉路口红绿灯控制问题,基于改进的BP神经网络算法,同时考虑关键车流和非关键车流信息,提出并设计了两级加权神经网络控制器来进行实时控制。仿真结果表明,本方法优于传统控制方法。     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

Control of magnetic bearing systems via the Chebyshevpolynomial-based unified model (CPBUM) neural network

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

多变量自适应PID型神经网络控制器及其设计方法

提出一种PID型神经网络控制器（PID-like Neural Network Controller，PIDNNC）及其设计方法．基于PID的简单结构和良好性能优势以及神经网络的自调节和自适应的特长，创建一种具有PID结构的多变量自适应的PID型神经网络控制器．该网络控制器的隐含层由带有输出反馈和激活反馈的混合局部连接递归网络组成．通过定义误差函数作为设计目标，采用弹性BP算法，并用变化率以及弹性BP算法中的符号法来处理某些求导关系，获得适于实时在线调整网络权值的修正公式．根据李亚普诺夫稳定性定理推导出确保控制系统稳定的学习速率的取值范围．最后通过实例进一步说明所提出网络控制器的优越性．     

基于Additive2multipl icative 模糊 神经网的ATM 网络拥塞控制

考虑了模糊神经网络的学习功能，提出利用Additive-multiplicative模糊神经网络(AMFNN)对ATM网络进行拥塞控制的方案．在拥塞控制过程中，利用AMFNN模糊神经网络预测下一个将要到达流的特征，结合当前缓冲区的队列信息预测网络是否发生拥塞．一旦预测出将有拥塞发生，控制器则向源端反馈拥塞控制信息，信源根据拥塞信息适当降低传输速率，从而避免了拥塞的发生．仿真结果表明，该方法可改善网络对拥塞的实时处理能力，提高网络资源的利用率．     

ATM congestion control using Minimal Resource Allocation Networks (MRAN)

This paper presents a congestion control scheme for ATM traffic using a minimal radial basis function neural network referred to as Minimal Resource Allocation Network (MRAN). Earlier studies have shown that MRAN is well suited for online adaptive control of nonlinear time varying systems as it can adjust its size by adding and pruning the hidden neurons based on the input data. Since ATM traffic is nonlinear and time varying performance of MRAN as a congestion controller is investigated here. These studies are carried out using OPNET to model the ATM traffic. The ATM traffic model consists of bursty, Variable Bit-Rate (VBR) and custom traffic in a multiplexed form so as to generate a heavily congested traffic situation. For this scenario, the controller has to minimize the congestion episodes and maintain the Quality of Service (QoS) requirements. This paper compares the performance of the MRAN congestion controller with that of a modified Explicit Rate Indication with Congestion Avoidance (ERICA) scheme and a Back-Propagation (BP) neural controller. Simulation results indicate that MRAN controller performs better than the modified ERICA and BP controller in reducing the congestion episodes and maintaining the desirable QoS.     

基于改进模糊神经网络的PID参数自整定

针对传统PID整定控制效果差且单纯神经网络整定存在参数学习和调整困难等问题,提出了一种基于改进模糊神经网络的PID参数整定方法。在该方法中,PID控制器的控制参数采用基于Mamdani模型的模糊神经网络进行自适应整定,模糊神经网络参数采用混沌遗传算法离线粗调和BP算法在线细调的方式进行学习和调整,仿真结果表明该整定策略动态响应快、误差控制精度高且网络中各节点及参数物理意义明确。最后分别从模糊规则数的变化及适应度函数的选取两方面提出两种优化方案,仿真结果表明增加模糊规则数或采用不同的适应度函数都有利于进一步减小控制误差。