Use of an Artificial Neural Network-based Metamodel to Reduce the Computational Cost in a Ray-tracing Prediction Model
Use of an Artificial Neural Network-based Metamodel to Reduce the Computational Cost in a Ray-tracing Prediction Model
复制标题
使用基于人工神经网络的元模型降低光线追踪预测模型的计算成本
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
W. P. Carpes
中科院分区：
文献类型：
--
作者：
S. S. Travessa;W. P. Carpes
本文的目的是基于分析使用RTQ3D（“准三维"射线追踪技术）来产生初始电磁场的值或适合于160个接收器，根据两个天线在封闭环境中被分布的可能位置。问题变量包括根据天线到基站的位置的一百六十个接收器的磁场值，其用作RMLP（人工神经网络，具有真实的反向传播学习算法的多层感知器）的算法的输入数据。与天线的位置相关联的磁场的值是要由网络学习的值，RMLP的老师。本研究的目的是开发有效的技术优化电磁问题。我们使用与基于ANN（人工神经网络）的元模型相关联的PSO（粒子群优化）算法。具体来说，我们使用MLP（多层感知器）与反向传播算法，以有效的方式评估目标函数。人工神经网络将被用来辅助“准三维”射线追踪技术，以减少粒子群优化技术的高计算成本。
The purpose of this article is based on analyzing the use of RTQ3D ("quasi-3D`` ray tracing technique) to produce the value of the initial electromagnetic fields or fitness for a hundred and sixty receivers according to the possible positions of two antennas to be distributed in a closed environment. The problem variables consist of the values of the magnetic fields for one hundred and sixty receptors depending on the positions of the antennas to the base stations, which serve as input data for the algorithm to the RMLP (Artificial Neural Network, multilayer perceptron with Real backpropagation learning algorithm). The values of the magnetic fields associated with the positions of the antennas are the values to be learned by the network, the teacher of RMLP. This study aims to develop efficient techniques for optimization of electromagnetic problems. We use the PSO (Particle Swarm Optimization) algorithm associated with a metamodel based on an ANN (Artificial Neural Network). Specifically, we use the MLP (Multilayer Perceptron) with the backpropagation algorithm in order to evaluate objective functions in an efficient way. The ANN will be used to assist the technique of "quasi 3D`` ray-tracing in order to reduce the high computational cost of this technique in PSO optimization.