Global polynomial synchronization of uncertain fractional variable-order BAM neural networks with proportional delay via quantized control

The objective of this work is to study the global polynomial synchronization (GPS) of fractional variable-order bidirectional associative memory neural networks (FVOBAMNNs) subjected to proportional delays and uncertain parameters. To deal with this challenge, a quantized controller incorporating linear error states and a non-smooth switching term is proposed. By leveraging this controller, GPS is attained by constructing an appropriate Lyapunov functional (LF) and applying suitable inequality-based techniques. The usefulness of the proposed synchronization approach is illustrated through two computational experiments.