Effective bandwidth approach to connection admission control formultimedia traffic in ATM networks

The authors propose an effective bandwidth approach to connection admission control in ATM networks. The aggregate arrival traffic is accurately modelled by a two-state Markov modulated Poisson process (MMPP) via the matching of four important statistics. If the buffer is large, admission control can be achieved by computing the effective bandwidth of the two-state MMPP. Simulation tests show that approach is simple and results in higher utilisation compared with conventional methods     

A real-time dynamic connection admission controller based ontraffic modeling, measurement, and fuzzy logic control

Broadband networks based on asynchronous transfer mode (ATM) have to support traffic with widely different traffic characteristics and quality of service requirement. In this paper, we extend our earlier work in and develop a dynamic connection admission controller (CAC) that supports cell loss requirements. The CAC algorithm explicitly computes the equivalent bandwidth required to support each class of connections based on on-line observations of aggregate traffic statistics as well as the declared parameters. We use Gaussian and diffusion approximations to characterize the aggregate traffic stream, and use fuzzy control strategy to combine model and measurement results to derive simple closed-form formulas to estimate the equivalent bandwidth in real time. We validate the proposed algorithms for various variable bit-rate traffic profiles and show that the system utilization can be substantially improved by appropriately tuning the fuzzy logic controller to combine traffic characteristics deduced from the declared parameters and traffic measurements     

Intelligent position/force controller for industrial robotmanipulators-application of fuzzy neural networks

An intelligent controller, which consists of an intelligent planner and an adaptive fuzzy neural position/force controller, is proposed for a robot manipulator. The proposed controller deals with the human expert knowledge and skills for planning and control. In this paper, it is applied to the task of deburring with an unknown object. The effectiveness of the proposed controller is evaluated by computer simulations     

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

A fuzzy neural network controller for parallel-resonant ultrasonicmotor drive

A newly designed driving circuit for the traveling-wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a current-source two-phase parallel-resonant inverter, is presented in this study. Moreover, since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a fuzzy neural network (NN) controller is proposed to control the USM drive system. In the proposed controller, a fuzzy model-following controller is implemented to control the rotor position of the USM, and an online trained NN with variable learning rates is implemented to tune the output scaling factor of the fuzzy controller. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the desired variable learning rates. From the experimental results, accurate tracking response can be obtained by the proposed controller, and the influences of parameter variations and external disturbances on the USM drive also can be reduced effectively     

Adaptive connection admission control for differentiated services access networks

In our earlier work, we have proposed some modifications for the bandwidth broker framework. With our modifications, it is possible to use measurement-based admission control in addition to the more traditional parameter-based admission control. Moreover, we have presented a new flexible admission control scheme that has proven to be very efficient in terms of bottleneck link utilization. Two problems, however, have arisen: the use of scheduling weights in admission control and bursty connection arrivals. In this paper, we present that the former one can be dealt with the use of adaptive scheduling weights, while the latter one can be fought with adaptive reservation limits. The proposed new algorithms are validated through simulations and their performance is compared against the nonadaptive basic scheme.     

一种基于L-M算法的组合神经网络模糊控制器

提出了一种基于L-M算法的神经网络模糊控制器。用两个相同的三层前向神经网络①和②来生成E和EC的隶属度,用三层前向神经网络③来实现模糊控制规则并生成控制输出,经过研究和对比选择了对于这3个网络来说最好的训练算法Levenberg-Marquardt算法。仿真结果表明了该控制器极好的控制性能。     

Overview of measurement-based connection admission control methods in ATM networks

An overview is given of previously-proposed measurement-based connection admission control (CAC) methods. First we address requirements for CAC methods, and then provide a taxonomy for CAC methods. Measurement-based CAC methods are discussed in detail, classified according to the taxonomy, and compared against each other with respect to the requirements. We conclude that measurement-based CAC methods based on effective bandwidth and bufferless models are promising because they do not require complex hardware and are less dependent on assumptions regarding traffic than methods in which the effect of buffer is considered.     

Applications of neural networks in high-speed communicationnetworks

Modern communication networks are expected to have hundreds or thousands of network nodes to which thousands of users are connected. Neural networks can contribute to this emerging new telecommunication infrastructure by providing fast, flexible, adaptive, and intelligent control. This article discusses the neural networks used for solving various control problems in high-speed communication networks     

A connection admission control scheme based on game theoretical model in ATM networks

In this paper, the main schemes of connection admission control (CAC) in ATM networks are briefly discussed especially the principle of dynamic bandwidth allocation. Then the fair share of the bandwidth among different traffic sources is analyzed based on cooperative game model. A CAC scheme is proposed using the genetic algorithm (GA) to optimize the bandwidth-delay-product formed utilization function that ensures the fair share and accuracy of accepting/rejecting the incoming calls. Simulation results show that the proposed scheme ensures fairness of the shared bandwidth to different traffic sources.     

Design of a real-time call admission controller for ATM networks

In this paper, we present a real-time computation algorithm based on the bufferless fluid flow model [Jabbari and Yegenolu, 1992] for call admission control (CAC) on one link of an asynchronous transfer mode (ATM) network with heterogeneous bursty traffic. Cell loss probability is adopted as the measure of quality-of-service (QoS). Our computation algorithm requires a constant memory size and needs only two multiplications and one division to determine whether a connection request can be accepted or not. It is known [Murase et al., 1991], that due to the interference between different types of traffic the individual cell loss probability may not meet the requirement even though the global one does. In this paper, we provide a close upper-bound for individual cell loss probability which can easily be obtained with our computation algorithm. Numerical examples using typical traffic parameters are studied to corroborate the upper-bound. We also compare the performance of the investigated CAC scheme with that of the effective bandwidth technique [Elwalid and Mitra, 1993]     

Service-aware based fuzzy admission control scheme in multi-service networks

Multi-service aggregated transmission is the direction of IP network. Providing different Quality of Service (QoS) assurance for different services has become a crucial problem in future network. Admission control is a vital function for multi-service IP network. This paper proposes a novel fuzzy admission control scheme based on coarse granularity service-aware technique. Different service has discriminative sensitivity to the same QoS characteristic parameter in general. The traffic class can be perceived by the service request parameter and the proposed QoS function. And requirements of dif- ferent applications can be met by maintaining the life parameter. From simulation results, the proposed scheme shows a better QoS provisioning than those traditional fuzzy logic based methods under the same admission probability.     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

Sliding mode controller by use of neural networks

This paper proposes a method to design a robust controller by use of a neural network. The trained neural network functions as a sliding mode controller which is robust against uncertainties. From the analysis of the neural network, it is proved that the switching surface is not the same as the sliding surface like conventional sliding mode control theory. The neural network shows that the switching surface should be a nonlinear surface because of a hard limitation on control inputs, even if the designed sliding surface is linear. From the result of estimating the robustness of neural networks, we propose that generalization of neural networks which are used as controllers should be measured by the robustness. Numerical simulations show that the controller is robust against uncertainties and robustness can be improved by the proposed method.     

Design of a fuzzy traffic controller for ATM networks

This paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traffic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only utilizes the mathematical formulation of classical control but also mimics the expert knowledge of traffic control. We appropriately choose input linguistic variables of the fuzzy traffic controller so that the controller is a closed-loop system with stable and robust operation. We extract knowledge of conventional control methods from numerous analytical data using a clustering technique and then use this knowledge to set parameters of the membership functions and fuzzy control rules via fuzzy set manipulation (linguistically stated but mathematically treated) with the aid of an optimization technique named genetic algorithm (GA). Simulation results show that the proposed fuzzy admission control improves system utilization by a significant 11%, while maintaining the quality of service (QoS) contract comparable with that of the conventional equivalent capacity method. The performance of the proposed fuzzy congestion control method is also 4% better than that of the conventional two-threshold congestion control method     

A high-speed GaAs MESFET optical controller

Optical interconnects are being considered for control signal distribution in phased array antennas. A packaged hybrid GaAs optical controller with a 1:16 demultiplexed output that is suitable for this application is described. The controller, which was fabricated using enhancement/depletion mode MESFET technology, operates at demultiplexer-limited input data rates up to 305 Mb/s and requires less than 200 μW optical input power     

一种基于模糊神经网络的非线性系统模型辨识方法

该文提出一种非线性系统的模型辨识方法。通过结构的辨识(学习)和参数的辨识(学习),构造了一个模糊神经网络,经调整网络的权值,获得一个精确的模糊模型。对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

Temporal signal processing with high-speed hybrid analog-digital neural networks

There are many problems which fall into the class of temporal signal processing. These problems have in common the need to relate the temporal properties of their inputs. Conventional solutions to these problems often have high hardware overhead, complex algorithmic solutions, or loss of information through the transformation of temporal properties of the input. To this end, a biologically motivated artificial and neural processing element has been developed. As in biological neurons, processing is time dependent and is implemented using both analog and digital techniques. These characteristics make the PE directly applicable a large class of temporal signal processing problems typically encountered in engineering and science. Multiple aspects of the PE behavior are adjustable, which produces a very wide range of behaviors from simple systems with only a few moderately connected processing elements. The processing element models are custom designed electric circuits based on basic CMOS components and therefore all developed systems can be directly implemented in any standard integrated CMOS technology. The integrated implementation, custom design, and a wide range of adaptable behaviors join to produce a very fast, low-hardware solution to complex spatiotemporal signal processing problems. Seven novel systems based on the hybrid PE are discussed as they relate to commonly encountered temporal signal processing problems.     

基才PSO—BP神经网络的PID控制器参数优化方法

针对传统PID控制系统参数整定过程存在的在线整定困难和控制品质不理想等问题,结合BP神经网络自学习和自适应能力强等特点,提出采用BP神经网络优化PID控制器参数。其次,为了加快BP神经网络学习收敛速度,防止其陷入局部极小点,提出采用粒子群优化算法来优化BP神经网络的连接权值矩阵。最后,给出了PSO—BP算法整定优化PID控制器参数的详细步骤和流程图。并通过一个PID控制系统的仿真实例来验证本文所提算法的有效性。仿真结果证明了本文所提方法在控制品质方面优于其它三种常规整定方法。     

A distributed algorithm of delay-bounded multicast routing formultimedia applications in wide area networks

Multicast routing is to find a tree which is rooted from a source node and contains all multicast destinations. There are two requirements of multicast routing in many multimedia applications: optimal network cost and bounded delay. The network cost of a tree is defined as the sum of the cost of all links in the tree. The bounded delay of a routing tree refers to the feature that the accumulated delay from the source to any destination along the tree shall not exceed a prespecified bound. This paper presents a distributed heuristic algorithm which generates routing trees having a suboptimal network cost under the delay bound constraint. The proposed algorithm is fully distributed, efficient in terms of the number of messages and convergence time, and flexible in dynamic membership changes. A large amount of simulations have been done to show the network cost of the routing trees generated by our algorithm is similar to, or even better than, other existing algorithms