Dynamic swarm learning for nanoparticles to control drug release function using RBF networks in atherosclerosis

Nanomedicine is an interdisciplinary research area that aims at prevention, diagnosis and treatment of complex diseases by the nanoscale operators to reduce side effects and increase the cure rate. Simplicity and limited functionality of these particles, as well as the decentralized computing and the uncertain dynamics of the human body environment are some of major challenges in this area. In this paper, we propose that equipping the nano-agents with learning ability provides high robustness against the uncertainties and changing dynamics of the human body. In particular, we propose a swarm of learning nano-agents for the treatment of Atherosclerosis. The swarm learns to approximate the desirable drug release function that changes in time according to the environmental conditions of the disease location. For this purpose, we use radial basis function neuron structures that can adapt with human body. Experimental results show the effectiveness of the proposed method in terms of disease control time and drug release rate, as well as robustness against possible disturbances.