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Congestion control of ATM networks using multilayer neural network approach: multiple source/single switch scenario

Congestion control of ATM networks using multilayer neural n...

引用

American Control Conference (ACC)

作者： S. Jagannathan J. Talluri Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Technology San Antonio TX USA

This paper proposes an adaptive control methodology using neural networks (NNs) for the available bit rate service class in an ATM network. Adaptive methodology is developed to control traffic where sources adjust their transmission rates in response to the feedback information from the network switches. Specifically, the ATM traffic at a given switch is modeled as a nonlinear discrete-time system, and a two-layer NN controller is designed to prevent congestion. Tuning methods are provided for the NN to estimate the unknown 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. No learning phase is required for the NN and initialization of the network weights is straightforward. Simulation results are provided to justify the theoretical conclusions for multiple source/single buffer scenario with and without feedback delays present.

关键词： Multi-layer neural network Neural networks Asynchronous transfer mode Communication system traffic control Traffic control Switches Adaptive control Quality of service Bit rate Adaptive systems

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A hybrid intelligent system for medical diagnosis

A hybrid intelligent system for medical diagnosis

引用

International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (纸本)0780370449

We propose a novel hybrid intelligent system (HIS) that is a combination of numerical and linguistic knowledge representation. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a fuzzy linguistic model optimized via the genetic algorithm. The ILFN is self-organizing network with the capability of fast, online, incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy and comprehensibility. The resulted HIS is capable of dealing with low-level numerical computation and higher-level linguistic computation. After the system completely constructed, it can incrementally learn new information in both numerical and linguistic structures. To evaluate the system's performance the well-known benchmark Wisconsin breast cancer data was studied as an application to medical diagnosis. The simulation results show that the proposed HIS perform better than the individual standalone systems.

关键词： Hybrid intelligent systems Medical diagnosis Fuzzy neural networks Knowledge representation Genetic algorithms Self-organizing networks System performance Breast cancer Computational modeling Medical simulation

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Coordination of exploration and exploitation in a dynamic environment

Coordination of exploration and exploitation in a dynamic en...

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Yen Fengming Yang T. Hickey M. Goldstein School of Electrical and Computer Engineering Intelligent Systems and Control Laboratory Oklahoma State University Stillwater OK USA

ISBN: (纸本)0780370449

One much researched issue in reinforcement learning is the trade off between exploration and exploitation. Being able to effectively balance exploration and exploitation activities becomes even more crucial in a dynamic environment. An algorithm is proposed herein that provides one solution to the exploration vs. exploitation dilemma. The algorithm is presented in the context of a path-finding agent in a dynamic grid-world problem. The state-value function used is penalty based, allowing the agent to act over the space of paths with minimal penalties. A forgetting mechanism is implemented that allows the agent to explore paths that were previously determined to be suboptimal. Simulation results are used to analyze the behavior of the proposed algorithm in a dynamic environment.

关键词： Learning systems Genetic algorithms systems engineering and theory intelligent systems intelligent control Control systems Analytical models Algorithm design and analysis Heuristic algorithms Acceleration

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Adaptive critic-based neural network object contact controller for a three-finger gripper

Adaptive critic-based neural network object contact controll...

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IEEE International Symposium on intelligent Control (ISIC)

作者： G. Galan S. Jagannathan Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Texas San Antonio San Antonio TX USA

MAR'S greenhouse operation requires robot arms that are capable of manipulating objects such as plant trays, fruits, vegetables and so on. Grasping and manipulation of objects have been a challenging task for robots. It is important that the manipulator performs these tasks accurately and faster with out damaging the object. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact subtask is defined in terms of following a trajectory accurately so that the object to be grasped is in contact with the gripper. The proposed controller scheme consists of a feedforward action generating neural network (NN) that compensates for the nonlinear gripper and object contact dynamics. The learning of this NN is performed online based on a critic signal so that a 3-finger gripper tracks a predefined desired trajectory, which is specified in terms of a desired position and velocity for object contact control. Novel weight tuning updates are derived for the action generating NN and a Lyapunov-based stability analysis is presented. Simulation results are shown for a 3-finger gripper making contact with an object.

关键词： Adaptive systems Programmable control Adaptive control Neural networks Grippers Robots Manipulators Feedforward neural networks Trajectory Velocity control

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Meta-reasoning for a distributed agent architecture

Meta-reasoning for a distributed agent architecture

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Southeastern Symposium on System Theory

作者： D. Chelberg L. Welch A. Lakshmikumar M. Gillen Qiang Zhou Laboratory for Intelligent Real-Time and Secure Systems Department of Electrical Engineering and Computer Science Ohio University Athens OH USA

ISBN: (纸本)0780366611

Agent based computing offers the ability to decentralize computing solutions by incorporating autonomy and intelligence into cooperating, distributed applications. It provides an effective medium for expressing solutions to problems that involve interaction with real-world environments and allows modelling of the world state and its dynamics. This model can be then used to determine how candidate actions affect the world and how to choose the best from a set of actions. Most agent paradigms overlook real-time requirements and computing resource constraints. We discuss the application of agent based computing to RoboCup and examine methods to improve it. In particular, we discuss the incorporation of a meta-level reasoning mechanism that handles individual agent organization, plan generation, task allocation, integration and plan execution. We also propose an architecture where a meta-agent is further enhanced by combining it with system-level resource allocation and optimization. The approach adopted by us unifies agent based computing with adaptive resource management for dynamic real-time systems. The goal is to build and implement a distributed, intelligent, agent based system for dynamic real-time applications.

关键词： Quality of service Real time systems Resource management computer architecture Distributed computing intelligent agent Monitoring Laboratories Application software Testing

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Light signaling for social interaction with mobile robots

Light signaling for social interaction with mobile robots

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： F. Michaud Minh Tuan Vu Laborius-Research Laboratory on Mobile Robotics and Intelligent Systems Department of Electrical and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada

To give autonomous mobile robots some kind of "social intelligence", they need to be able to recognize and interact with other agents in the world. This paper describes how a light signaling device can be used to identify another individual and to communicate simple information. By having the agents relatively close to each other, they share the same perceptual space, which allows them to sense or deduce implicit information concerning the context of their interaction. Using a vision- and sonar-based Pioneer I robot equipped with a colored-light signaling device, experimental results demonstrate how the robot. can interact with a human interlocutor in a ball-passing game.

关键词： Mobile robots intelligent robots intelligent agent Human robot interaction Humanoid robots Robot sensing systems Orbital robotics Animals Robot vision systems Protocols

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Dimensions of adjustable autonomy

引用

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

作者： Barber, K. Suzanne Martin, Cheryl E. Reed, Nancy E. Kortenkamp, David Laboratory for Intelligent Processes and Systems Electrical and Comp. Eng The University of Texas at Austin AustinTX78712 United States Dept. of Computer and Info. Sci Linköping University SE-58183 Sweden NASA Johnson Space Center-ER2 HoustonTX77058 United States

ISBN: (纸本)3540425977

This panel discussion focused on the dimensions of autonomy that are defined and adjusted by the research of the workshop participants. In particular, the groups were able to explain and relate their work in adjustable autonomy to each other’s work, although the topics and application areas are very diverse. We describe three dimensions of an agent’s autonomy that are adjustable - the agent’s independence in how it carries out its goals, its control over choosing between options to carry out its goals, and the set of goals it has to achieve. Each of these dimensions of autonomy can be adjusted over time. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Autonomous agents

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Mobile robot that can read symbols

Mobile robot that can read symbols

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： F. Michaud D. Letourneau LABORIUS-Research Laboratory on Mobile Robotics aid Intelligent Systems Department of Electrical and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada

To give an autonomous robot the ability to read symbols, we need to integrate character recognition techniques with methods to position the robot in front of the symbol, to capture the image that will be used in the identification process, and to validate the overall approach on a robot. Our goal is to address the different aspects required in making a mobile robotic platform recognize symbols placed in real world environment, using current hardware and software capabilities. Validated on a Pioneer 2 robot, the approach described in this paper uses colors to detect symbols, a PID controller to position the camera, simple heuristics to select image regions that could contain symbols, and finally a neural system for symbol identification. Results in different lighting conditions are presented.

关键词： Mobile robots intelligent robots Robot vision systems Hardware Cameras Roads Sonar Mobile computing intelligent systems Character recognition

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Developing a traceable domain reference architecture to support clinical trials at the National Cancer Institute. An experience report

Developing a traceable domain reference architecture to supp...

引用

International Conference and Workshop on engineering of computer-Based systems

作者： K.S. Barber T. Graser J. Silva Laboratory for Intelligent Proc. and Systems Electrical and Computer Engineering University of Texas Austin Austin TX USA National Cancer Institute Bethesda MD USA

ISBN: (纸本)0769510868

Presents the requirements gathering and software architecture derivation approach developed by the University of Texas at Austin and leveraged by the National Cancer Institute (NCI) in their efforts to automate the creation, management and evaluation of clinical trials. NCI must face the complexity of managing clinical trials and of coordinating large numbers and varied types of stakeholders. SEPA (systems engineering Process Activities), from the University of Texas at Austin, is well-suited to address domain modeling and software development at NCI due to a strong emphasis on explicit traceability from a derived architecture to individual knowledge acquisition sessions, as well as facilitated resolution among conflicting stakeholder contributions. Specifically, this paper focuses on the SEPA Domain Reference Architecture (DRA), a software architecture designed to capture domain requirements (i.e. domain data, functionality and timing).

关键词： Clinical trials Cancer computer architecture Software architecture Software development management Knowledge engineering systems engineering and theory Programming Knowledge acquisition Software design

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Inverse kinematics learning by modular architecture neural networks with performance prediction networks

Inverse kinematics learning by modular architecture neural n...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： E. Oyama Nak Young Chong A. Agah T. Maeda Intelligent Systems Laboratory National Institute of Advanced Industrial Science and Technology Ibaraki Japan Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA School of Engineering University of Tokyo Tokyo Japan

ISBN: (纸本)0780365763

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

关键词： Kinematics Neural networks Artificial neural networks Multi-layer neural network computer networks Robots computer architecture Manipulators Concurrent computing Parallel processing

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