{'fraction_non_alphanumeric': 0.02159827213822894, 'fraction_numerical': 0.004859611231101512, 'mean_word_length': 5.035830618892508, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
CNN Template Learning To Obtain Desired Images Using Back Propagation Algorithm

Cellular neural networks (CNN) were introduced by Chua and Yang in 1998 [1]. The idea of the CNN was inspired from the architecture of the cellular automata and the neural networks. Unlike the conventional neural networks, the CNN has local connectivity property. Since the structure of the CNN resembles the structure of animals retina, the CNN can be used for various image processing applications [2]-[4]. Wiring weights of the cells of the CNN are established by parameters called the template. The template is most important parameter, because performance of the CNN is decided by the template. Thus, some template design methods as template learning using GA algorithm are proposed. These works are important subject in the studies of the CNN [5]. In this paper, template learning of cellular neural networks using back propagation algorithm is proposed. In our proposed CNN, we build back propagation algorithm into the feedback part of CNN. Back propagation algorithm is inspired form back propagation neural networks [6]. Back propagation neural networks operates with a feed forward neural network which is composed of an input layer, a hidden layer and an output layer, and the effectiveness of the back propagation algorithm has been confirmed in learning performance. In this paper, the template of CNN is dynamically updated in each step using the back propagation algorithm. Computer simulations using onedimensional image show that the back propagation algorithm is effective for template learning of CNN. At the moment, we do not say that the proposed template learning method exhibited a superior performance than the other template learning method. However, we feel that we obtained some results to broaden the research on the template learning of CNN.