{'fraction_non_alphanumeric': 0.021341463414634148, 'fraction_numerical': 0.0, 'mean_word_length': 5.98936170212766, '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}}
New delay-dependent robust stability condition for neutral-type neural networks with mixed time delays

The robust exponential stability is investigated for a class of uncertain neutral-type neural networks with both variable and distributed time-varying delays. By introducing a new vector Lyapunov-Krasovskii functional, using Jensen integral inequality and linear matrix inequality(LMI) techniques, two delay-dependent sufficient criteria are obtained for exponential stability of considered neural networks, which generalize some previous results in the literature. Three examples are given to show the less conservativeness of the obtained conditions.