Design of H∞ State Estimation Algorithm for Delayed Memristive Neural Networks with Variance Constraints and DoS Attack under Encoding-Decoding Scheme - Li, Jiaying; Gao, Yan; Du, Junhua; Li, Jiaxing; Liu, Sitong; Ali, Mahliya; Pazlamu, Halisa (2025 7th International Conference on Data-driven Optimization of Complex Systems (DOCS), 2025)
This paper considers the design of varianceconstrained H_{∞} state estimation algorithm for delayed memristive neural networks subject to DoS attack under encodingdecoding scheme. To ensure the security of data transmission, an encoding-decoding communication mechanism is utilized in the transmission channel, which is capable of encoding the transmitted data with a specified number of bits. Our aim is to propose a new H_{∞} state estimation algorithm, where the error variance boundedness and H_{∞} performance requirement are both guaranteed through the establishment of certain sufficient conditions. In the end, a simulation experiment is conducted to validate the effectiveness and practicality of the newly presented H_{∞} state estimation algorithm.