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Faster response in bounded-update-rate, discrete-time linear networks using delayed self-reinforcement

2019; Taylor & Francis; Volume: 94; Issue: 5 Linguagem: Inglês

10.1080/00207179.2019.1644537

1366-5820

Santosh Devasia,

Nonlinear Dynamics and Pattern Formation

The response speed of a network impacts the efficacy of its actions to external stimuli. However, for a given bound on the update rate, the network-response speed is limited by the need to maintain stability. The main contributions of this work are (i) to use delayed self-reinforcement (DSR), where each agent uses its current and previously available information to strengthen its update, (ii) develop conditions for stability with the new update, and (iii) to show that such self-reinforcement can increase the network speed without the need to increase the individual agent's update rate. Example simulation results are presented that show more than an order of magnitude improvement in the response speed (quantified using the settling time) with the proposed DSR approach.