Fault detection filtering for memristive neural networks in the presence of communication constraints

This study aims to address the fault detection filtering issue for a kind of memristive neural networks in the presence of communication constraints. To mitigate the burden of communication transmission, a dynamic quantizer is employed to quantize the measurement output instead of a static one. The updating law of the dynamic quantizer is regulated by the quantization range to enhance network transmission efficiency while ensuring system performance. A parameter -based dynamic event-triggered mechanism is established based on the correlation between the triggering criterion and updating law of the dynamic quantizer. This mechanism determines whether to broadcast measurements and which measurements to broadcast, as opposed to relying on fixed reporting schedules. Additionally, to deal with the mismatch between the stochastic communication protocol and filter modes, an asynchronous fault detection filter is presented. Using Lyapunov theory, sufficient conditions are attained to guarantee the stochastic stability of the filtering error systems. Finally, a numerical example is provided to demonstrate the effectiveness of the filter design scheme.