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ROBUST CONGESTION CONTROL IN HIGH SPEED COMMUNICATION NETWORKS: A MODEL PREDICTIVE CONTROL APPROACH

IFAC Proceedings Volumes 2002年第1期35卷 103-108页

作者： Hua O. Wang Yongru Gu Huajing Fang Laboratory for Intelligent and Nonlinear Control (LINC) Department of Electrical and Computer Engineering Duke University Durham NC 27708-0291 USA Center for Nonlinear and Complex Systems Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. At a bottleneck node, there are multiple best-effort sources competing with other high priority cross traffic sources. The goal of congestion control is to achieve high link utilization, low packet loss, low delay, and fairness among the best-effort sources. In this paper, the high priority traffic is described by an autoregressive integrated moving average (ARIMA) process. To deal with the propagation delays associated with the best-effort sources, model predictive control, particularly, generalized predictive control, techniques are proposed to solve the congestion problem here. It is demonstrated that the proposed controller performs well and is robust to delay uncertainties. In addition, in a multiple-nodes configuration, the controller provides max-min fairness.

关键词： Communication control applications Model-based control Predictive control Communication networks Time delay Robustness

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An efficient approach of the SOM algorithm to the traveling salesman problem

An efficient approach of the SOM algorithm to the traveling ...

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Brazilian Symposium on Neural Networks

作者： F. Vieira A. Neto J. Costa Computer Engineering and Automation Department Technological Center Intelligent Systems Laboratory Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Electrical Engineering Department Intelligent Systems Laboratory Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil

This paper presents an approach to the well-known traveling salesman problem (TSP) via competitive neural networks. The neural network model adopted in this work is the Kohonen network or self-organizing maps (SOM), which has important topological information about its neurons configuration. This paper is concerned with the initialization aspects, parameters adaptation, and the complexity analysis of the proposed algorithm. The modified SOM algorithm proposed in this paper has shown better results when compared with others neural network based approaches to the TSP.

关键词： Traveling salesman problems Neurons Neural networks Cities and towns Convergence Joining processes Automation intelligent systems Self organizing feature maps Algorithm design and analysis

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A new model of the visual feedback coordinate transformation in humans based on disturbance noise and feedback error that accounts for time delays

A new model of the visual feedback coordinate transformation...

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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： E. Oyama K.F. MacDorman T. Maeda S. Tachi A. Agah Science and Technology (AIST) National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Intelligent Robotics Laboratory Graduate School of Systems Engineering Wakayama University Wakayama Japan School of Engineering University of Tokyo Bunkyo Tokyo Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

An essential factor in understanding the motor learning capability of humans, is the coordinate transformation learning of the visual feedback controller. Although a number of learning models for the visual feedback controller have been proposed, none has been able to establish a definitive approach. In our previous work, we have suggested a learning model that uses disturbance noise and the feedback error signal to learn the human visual feedback controller's coordinate transformation. However, the model does not fully consider the time delay in the visual feedback control loop. This paper presents a modified learning model taking into account the time delay and the convergence properties of the model. Numerical simulations are presented to illustrate the effectiveness of the proposed approach.

关键词： Humans Delay effects Adaptive control Error correction Biological system modeling Feedback control Convergence Kinematics intelligent robots Velocity control

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Statistical approximation learning of discontinuous functions using simultaneous recurrent neural networks

Statistical approximation learning of discontinuous function...

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IEEE International Symposium on intelligent Control (ISIC)

作者： M. Sakai N. Homma M.M. Gupta K. Abe Department of Computer and Mathematical Sciences Graduate School of Information Sciences University of Tohoku Sendai Japan Department of Radiological Technology College of Medical Sciences University of Tohoku Sendai Japan Intelligent Systems Research Laboratory College of Engineering University of Saskatchewan Saskatoon SAS Canada Department of Electrical and Communication Engineering Graduate School of Engineering University of Tohoku Sendai Japan

In this paper, we develop an architecture for a novel type of neural network which is known as simultaneous recurrent neural networks (SRNNs). Using this novel neural architecture, we propose a statistical approximation learning (SAL) method. The SRNNs have the capability to approximate non-smooth functions which cannot be approximated by using conventional multilayer perceptrons. However, most of the learning methods for the SRNNs are computationally expensive due to their inherent recursive calculations. To solve this problem, as an approximation learning method, the SAL method is proposed by using a statistical relation between the time-series of the network outputs and the network configuration parameters. Simulation results show that SRNNs trained by the proposed SAL method can learn a strongly nonlinear function efficiently within a practical computation time.

关键词： Recurrent neural networks Biological neural networks Neural networks Learning systems Educational institutions Biomedical engineering Multilayer perceptrons Computational modeling Multi-layer neural network Feedforward neural networks

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Belief revision process based on trust: Agents evaluating reputation of information sources 1

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Workshop on Deception, Fraud and Trust in Agent Societies, 2000 held as part of the Autonomous Agents Conference, 2000

作者： Barber, K. Suzanne Kim, Joonoo The Laboratory for Intelligent Processes and Systems Electrical and Computer Engineering The University of Texas at Austin AustinTX78722 United States

ISBN: (数字)9783540455479

ISBN: (纸本)3540430695

In this paper, we propose a multi-agent belief revision algorithm that utilizes knowledge about the reliability or trustworthiness (reputation) of information sources1. Incorporating reliability information into belief revision mechanisms is essential for agents in real world multi-agent systems. This research assumes the global truth is not available to individual agents and agents only maintain a local subjective perspective, which often is different from the perspective of others. This assumption is true for many domains where the global truth is not available (or infeasible to acquire and maintain) and the cost of collecting and maintaining a centralized global perspective is prohibitive. As an agent builds its local perspective, the variance on the quality of the incoming information depends on the originating information sources. Modeling the quality of incoming information is useful regardless of the level and type of security in a given system. This paper introduces the definition of the trust as the agent’s confidence in the ability and intention of an information source to deliver correct information and reputation as the amount of trust an information source has created for itself through interactions with other agents. This economical (or monetary) perspective of reputation, viewing reputation as an asset, serves as social law that mandates staying trustworthy to other agents. Algorithms (direct and indirect) maintaining the model of the reputations of other information sources are also introduced. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Bayesian networks

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A communication protocol supporting dynamic autonomy agreements in multi-agent systems 6th

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6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000

作者： Suzanne Barber, K. Martin, Cheryl E. McKay, Ryan M. The Laboratory for Intelligent Processes and Systems ENS 22 Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States

ISBN: (纸本)3540425977

Given the challenges faced by agent-based systems including dynamically changing environments, uncertainty, and failures, agent research must explore techniques to make these systems ever more flexible and adaptive. Adaptive systems are more robust and often perform better given changing situations. One area of research that offers promise for improved agent-based systems is adaptive autonomy. In general, adaptive autonomy gives agents the ability to seek help to solve problems or take initiative when otherwise they would be constrained by their design to follow some fixed procedures or rules for interacting with other agents. In order to gain run-time flexibility and any associated performance improvements, agents must be able to adapt their autonomy during system operation. This paper provides a mechanism through which agents can achieve this end. The examples presented in this paper focus on adapting one dimension of autonomy, specifically the decision-making framework under which agents solve problems in their system. The communication protocol presented here allows agents to dynamically modify their autonomy at run-time by forming agreements with other agents about autonomy-altering interactions. Conversation state machines and example event traces are provided. This communication protocol forms a solid foundation around which systems capable of dynamic adaptive autonomy can be implemented. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Multi agent systems

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Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.

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International journal of neural systems 2001年第5期11卷 427-443页

作者： Yen, G.G. Meesad, P. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater 74078 OK United States

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

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Autonomy as decision-making control

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7th International Workshop on Agent Theories Architectures and Languages, ATAL 2000

作者： Barber, K. Suzanne Martin, Cheryl E. Laboratory for Intelligent Processes and Systems University of Texas at Austin Department of Electrical and Computer Engineering 201 East 24th Street ACE 5.402 Austin TX 78712 United States

Autonomy is a very complex concept. This discussion develops a definition for one dimension of autonomy: decision-making control. The development of this definition draws salient features from previous work. Each stag...

ISBN: (纸本)3540424229

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Autonomy as decision-making control

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7th International Workshop on Agent Theories Architectures and Languages, ATAL 2000

作者： Suzanne Barber, K. Martin, Cheryl E. The Laboratory for Intelligent Processes and Systems The University of Texas at Austin Department of Electrical and Computer Engineering 201 East 24th Street ACE 5.402 AustinTX United States

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Specification and Modeling of Network Resources in Dynamic, Distributed Real-time System 14

Specification and Modeling of Network Resources in Dynamic, ...

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14th International Conference on Parallel and Distributed Computing systems, PDCS 2001

作者： Tong, Lu Bruggeman, Carl Tjaden, Brett Chen, Hong Welch, Lonnie R. Laboratory for Intelligent Real-time Secure Systems School of Electrical Engineering and Computer Science Ohio University AthensOH45701 United States

ISBN: (纸本)9781618395740

This paper presents a system model and specification language for network resource management in dynamic, distributed real-time systems. The model is used to define techniques for QoS (quality-of-service) monitoring, QoS diagnosis, and schedulability analysis of network resources. Preliminary experimental results show the effectiveness of the extension for specification of realtime network resources, detection and diagnosis of network resources bottlenecks, and restoration of acceptable QoS via reallocation of distributed computer and network resources. © (2001) by the International Society for computers and Their Applications All rights reserved.

关键词： Specification languages

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