Sotirov Sotir, Atanassova Vassia, Sotirova Evdokia, Doukovska Lyubka, Bureva Veselina, Mavrov Deyan, Tomov Jivko
Laboratory of Intelligent Systems, University "Prof. Dr. Assen Zlatarov", 1 "Prof. Yakimov" Blvd., 8010 Burgas, Bulgaria.
Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, 105 "Acad. G. Bonchev" Str., 1113 Sofia, Bulgaria.
Comput Intell Neurosci. 2017;2017:2157852. doi: 10.1155/2017/2157852. Epub 2017 Aug 10.
The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images.
应用互准则分析（ICA）方法的目的是减少神经网络输入端的变量集，因为变量数量众多会增加网络中的神经元数量，从而使其无法用于硬件实现。在此，首次借助ICA方法获得了神经网络训练输入参数三元组之间的相关性。在这种情况下，我们使用ICA方法进行数据预处理，这可能会减少神经网络的总训练时间，进而减少网络处理数据和图像的时间。
