Continuous-time distributed estimation

Adaptive diffusion models endow networks with distributed learning and cognitive abilities. These models have been applied recently to emulate various forms of complex and self-organized patterns of behavior encountered in biological networks. In diffusion adaptation, nodes share information with their neighbors in real-time, and the network evolves towards a common objective through decentralized coordination and in-network processing. Current models are based on discrete-time adaptive diffusion strategies. However, physical phenomena usually are governed by continuous-time dynamics. In this paper, we derive continuous-time diffusion adaptive algorithms, which can help provide more accurate models for exchanges of information, and also for systems with large variations in their time constants.