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首页> 外文会议>The 2nd International Workshop on Artificial Intelligence and Mathematical Methods in Pavement and Geomechanical Systems, Aug 11-12, 2000, Newark, Delaware, USA >Adaptive logic applications in pavement performance modeling
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Adaptive logic applications in pavement performance modeling

机译：自适应逻辑在路面性能建模中的应用

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摘要

Pavement performance is one of the most important components of the pavement management system. Models for predicting pavement performance have been developed on the basis of traffic and time-related models, interactive time traffic or distress models. The characteristic feature of the models is that they are formulated and estimated statistically, recently however artificial neural networks are being used. The purpose of this paper is to extend the use of the adaptive logic networks in pavement performance modeling, by investigating the effect of different learning rates of adaptive logic networks in pavement performance modeling. Adaptive logic networks (ALN) has recently emerged as an effective alternative to artificial neural networks for machine learning tasks. Adaptive logic networks (ALN) is a network arranged as a binary tree, where the processing element has exactly two inputs and one output. Each processing element, or node, computes one of the four logical functions AND, OR, RIGHT (MAX) and LEFT (MIN). The root node of the tree represents the output of the network, while the leaves of the tree are connected to the input variables and /or their complements. It has been shown that such a tree can be constructed to compute any Boolean function for arbitrary number of input variables.
机译：路面性能是路面管理系统最重要的组成部分之一。在交通和与时间有关的模型，交互式时间交通或遇险模型的基础上，开发了用于预测路面性能的模型。该模型的特征是可以对它们进行统计和统计估计，但是最近正在使用人工神经网络。本文的目的是通过研究自适应逻辑网络的不同学习率在路面性能建模中的作用，来扩展自适应逻辑网络在路面性能建模中的应用。自适应逻辑网络（ALN）最近已成为机器学习任务的人工神经网络的有效替代品。自适应逻辑网络（ALN）是安排为二叉树的网络，其中处理元素恰好具有两个输入和一个输出。每个处理元素或节点计算四个逻辑功能AND，OR，RIGHT（最大）和LEFT（最小）之一。树的根节点表示网络的输出，而树的叶子则连接到输入变量和/或其补数。已经表明，可以构造这样的树以针对任意数量的输入变量计算任何布尔函数。

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