{'fraction_non_alphanumeric': 0.02006980802792321, 'fraction_numerical': 0.0, 'mean_word_length': 5.302197802197802, '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}}
Non-fragile Set-membership State Estimation for Memristive Neural Networks with Incomplete Measurements via Round-robin Protocol

In this paper, the non-fragile set-membership state estimation method is provided for discrete delayed memristive neural networks with incomplete measurements under the round-robin (RR) protocol, where the nonlinear neuron activation function satisfying the sector-bounded condition is considered. The RR protocol is considered to depict that the measurements can be transmitted in an orderly manner so as to save limited network resources. The main purpose of this paper is to present a non-fragile state estimation approach such that, under the premise of unknown but bounded noise and RR protocol, the estimation error at each moment is enclosed in the ellipsoid set. Moreover, a convex optimization method is proposed to minimize the obtained ellipsoids. Also, the desirable estimator gain matrices can be found by in terms of the solutions to a series of the recursive linear matrix inequalities. At last, the feasibility of the proposed estimator design technique is illustrated via using a simulation example.