A Classification Model of Identifying Weeds Based on Chaotic Neural Network and the Empirical Mode Decomposition

Abstract

This paper puts forward a classification method of imitating the human eyes to recognize image as a whole which combined chaotic neural network and the Empirical Mode Decomposition (EMD). The method takes the individual of weeds plant as the research object and utilizes the chaotic neural network, the EMD, Teager energy operator and cluster analysis technology comprehensively. Firstly take the two-dimensional gray image matrix as the chaotic neural network weight matrix directly. Use the chaotic neural network with n neurons expression to iterate and get the one-dimensional output curve. Secondly, make the EMD decomposition for the curve and get the corresponding intrinsic mode function (IMF) curves. Then make the Teager energy transformation for each IMF component and get the average Teager energy. Finally use the fuzzy clustering algorithm to cluster analyze and get the clustering results. It can realize the classification of different categories of weeds through analysis and contrast the results with the original images. The experiment proves the effectiveness of the proposed algorithm for classifying weeds, and it is a universal new method of weeds classification.