{'fraction_non_alphanumeric': 0.023316062176165803, 'fraction_numerical': 0.0, 'mean_word_length': 5.441666666666666, '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}}
Robust Stability of Discrete-Time Stochastic Neural Networks with Markovian Jumping Parameters and Mode-Dependent Delays

This paper investigates the problem of robust stability for a class of uncertain discrete-time stochastic recurrent neural networks (RNNs) with Markovian jumping parameters and mode-dependent delays. Stochastic item is nonlinear, and jumping parameters are considered as discrete time, discrete-state Markov process. We can get novel robust stability conditions in terms of linear matrix inequality (LMI) approach. Furthermore, we will introduce into some free weighting matrices by adding to a zero item in order to lead to much less conservative results. At last, a numerical example is given to illustrate the effectiveness of the proposed method.