Synchronization transient and stability analysis of fractional neural networks with intermittent control based on its memory characteristic

For any system in nature, when its input changes, there must be a transient oscillation process. However, this important process is usually neglected in many achievements. In this paper, we have studied the synchronization transient and the stability of fractional neural networks with intermittent control. Firstly, the relation between the fractional model and the system impulsive response is discussed. Secondly, the intermittent signal is treated as a piecewise continuous signal, and the bridge between the output and the historical input is established. Lastly, the transient process and the stability are analyzed according to the output. The research shows that the transient process relates to all historical input, the memory‐decaying characteristic, and control parameters. The transient of a neural unit will have effect on the other nets. This result is highly consistent with the real physical process. The obtained theoretical achievements are verified by some examples in this paper.