{'fraction_non_alphanumeric': 0.016518424396442185, 'fraction_numerical': 0.0, 'mean_word_length': 5.304, '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}}
CMOS analog integrated circuit for fuzzy c-means clustering

At present, neurofuzzy techniques are attractive to approximate pattern recognition solutions when they are implemented as parallel systems with adaptive capability. in particular, the clustering of data groups and their recognition by the fuzzy c-means approach is very efficient in this type of systems. In this work we show the parallel analog circuits in CMOS technology dedicated to compute in real-time the fuzzy c-means algorithm. The circuits are composed by MOS transistors working in weak inversion regime for current-mode signal representation, which reduces active area and interconnection complexity. The signal flow in this silicon chip is derived from a layer structure with specific arithmetic operation nodes.