期刊名称：Eastern-European Journal of Enterprise Technologies
印刷版ISSN：1729-3774
电子版ISSN：1729-4061
出版年度：2017
卷号：6
期号：9
页码：63-69
DOI：10.15587/1729-4061.2017.116134
语种：English
出版社：PC Technology Center
摘要：A promising class of neural network models used recently to solve the problems of recognition of noisy images are denoising autoencoders. In particular, the evolutionary approach can be effectively used in DAE to determine the network architecture, weights and learning algorithm. The proposed neural network evolving autoencoder allows efficient processing of noisy images due to the iterative learning procedure even in the presence of local distortions. When using the EDAE for determining the network architecture, weights and learning algorithm, standard evolutionary procedures (population initialization, population assessment, selection, crossover, mutation), as well as the evolutionary algorithm for the ANN adjustment and special chromosome formats are used.The proposed approach to filtering and recognition of noisy images based on the EDAE application is promising for environmental monitoring of landscape and industrial areas.
