CAREER: Neural Network-Based Adaptive Critic Designs for Broadband Network Traffic Control

自适应临界设计（ACD）是在一般情况下近似动态规划的设计。典型的ACD由三个可以使用神经网络实现的模块组成-值模块，预测模块和决策模块。该项目的目的是与业界合作，在开发和实施学习业务控制计划的宽带通信网络使用ACD的。一个开放的和具有挑战性的问题，面临着下一代通信网络的设计师是设计方案，有效地集成多媒体流量，并保证服务质量（QoS）为每个流量源。这里的挑战是最大限度地提高网络带宽利用率，同时保证QoS。为此目的，过去已经广泛地研究了包括呼叫准入控制和业务强制的业务控制方案。然而，大多数现有方案牺牲了带宽利用率（即，浪费网络资源/损失收入）以保证QoS。该项目的初步研究表明，在相同的QoS目标下，使用基于ACD的自学习方法可以获得比现有方案更高的带宽利用率。该项目将实现不同级别（分组/呼叫/网络级别）的集成自学习流量控制。这些包括具有优化参数的令牌桶、呼叫准入控制以及路由和拥塞控制。基于神经网络的ACD领域中的两个基本问题将被研究：（1）基于神经网络的ACD的结构鲁棒性分析和（2）与神经网络训练中的收敛有关的问题。教育活动包括：（1）课程、实验室和课程开发：开发关于ACD的研究生课程，开发史蒂文斯的智能系统实验室，以及强调ACD/类脑智能系统的跨学科努力;（2）本科生的研究经验：让本科生接触各种ACD研究领域的众多机会;以及（3）教学举措：开发一个网上互动计算机工具，用于自动提交作业和评分，并在计算机网络上实施一个白色板系统，以帮助学生学习;对教学举措的评价将使用一个网上在线教育评估系统，在该系统中，应用ACDs的自学功能，以做出最佳决策。

9874601LiuAdaptive critic designs (ACDs) are designs that approximate dynamic programming in the general case. A typical ACD consists of three modules that can be implemented using neural networks -- a Values module, a Prediction module , and a Decision module. This project aims to collaborate with industry in developing and implementing learning traffic control schemes for broadband communication networks using ACD's.An open and challenging problem facing the designer of the next generation of communication networks is to design schemes that integrate multimedia traffic efficiently and that guarantee the quality of service (QoS) for each traffic source. The challenge here is to maximize the network bandwidth utilization and at the same time to guarantee QoS. For this purpose, traffic control schemes including call admission control and traffic enforcement have been studied extensively in the past. However, most of the existing schemes sacrifice bandwidth utilization (i.e., waste network resources/lose revenue) in order to guarantee QoS. The preliminary study of this project has shown that, with the same QoS goals, higher bandwidth utilization than existing schemes can be achieved using a self-learning approach based on ACDs.Integrated self-learning traffic control at different levels (packet/call/network levels) will be implemented in this project. These include token bucket with optimized parameters, call admission control, and routing and congestion control. Two fundamental issues in the field of neural network-based ACDs will be investigated: (1) structural robustness analysis of neural network-based ACDs and (2) problems relating to convergence in neural network training. Education activities include: (1) Course, laboratory and curriculum development: To develop a graduate course on ACDs, to develop the Intelligent Systems Laboratory at Stevens, and the interdisciplinary effort which will emphasize ACDs/brain-like intelligent systems; (2) Research experience for undergraduate students: To expose undergraduate students to the numerous Opportunities available in a variety of ACDs research areas; and (3) Pedagogical initiatives: To develop a web-based interactive computer tool for automated homework submission and grading and to implement a white board system over the computer network to aid student learning; the evaluation of the pedagogical initiatives will use a web-based, on-line education assessment system in which the self-learning feature of ACDs is applied to optimal decision making.***