Analyzing the dynamics of cellular flames using Karhunen-Loeve decomposition and autoassociative neural networks

摘要

Video data from experiments on the dynamics of two dimensional flames are analyzed. The tools used are Karhunen-Loeve (K-L) decomposition and autoassociative neural networks (ANN). The K-L decomposition, known for its wide applications in scientific problems for data compression, noise filtering, and feature identification, is used to determine an intrinsic coordinate system or orthogonal eigenfunctions that best represent the flame data set. Five eigenfunctions are retained and the rest are disregarded so that reconstructions of the flame data based on the retained eigenfunctions capture most of the dynamics from the original data. The time dependent data coefficients in the expansion of the flame data are used to develop and train an ANN with the task of reducing the dimensionality of the dynamics into a space which reflects the intrinsic dimensionality of the problem. [References: 28]