基于人工鱼群算法的电容层析成像图像重建
n Image Reconstruction Algorithm Based on Artificial Fish-Swarm for Electrical Capacitance Tomography System
人工鱼群算法（AFSA）是一种新型的基于动物行为的自治体寻优策略，本文尝试将人工鱼群算法引入到神经网络的训练过程中，提出了一种基于人工鱼群算法优化径向基(RBF)神经网络的电容层析成像算法，给出了算法的数学模型，并形成了一种新的RBF网络训练算法，与传统的神经网络算法进行比较，结果表明基于AFSA的RBF神经网络的ECT图像重建算法具有误差小、质量高及收敛速度快等优点，为ECT图像重建算法的研究提供了一个新的思路。
rtificial Fish-Swarm Algorithm (AFSA) is a novel autonomic agent optimizing strategy based on animal behavior. In this paper AFSA algorithm is introduced into artificial neutral network so as to propose an algorithm based on AFSA optimizing radial basis function (RBF) neutral network. The mathematical model of this algorithm is given followed an introduction to the basic principle of 12-electrode electrical capacitance tomography. Experiments that compared classical artificial neutral network algorithm with the AFSA algorithm showed that AFSA optimizing RBF neutral network based ECT image reconstruction algorithm has the advantages such as minor error and high convergence speed, and therefore provides a new idea for the research on ECT image reconstruction algorithm.
陈德运、王莉莉、陈兰、周婧
电气测量技术、电气测量仪器自动化技术、自动化技术设备计算技术、计算机技术
电容层析成像图像重建人工鱼群RBF神经网络
electrical capacitance tomographyimage reconstructionartificial fish-swarmRBF neural network
陈德运,王莉莉,陈兰,周婧.基于人工鱼群算法的电容层析成像图像重建[EB/OL].(2009-06-18)[2025-08-10].http://www.paper.edu.cn/releasepaper/content/200906-544.点此复制
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