Neural Networks-Based Immune Optimization Regulation Using Adaptive Dynamic Programming

Abstract

AbstractThis chapter investigates optimal regulation scheme between tumor and immune cells based on ADP approach. The therapeutic goal is to inhibit the growth of tumor cells to allowable injury degree, and maximize the number of immune cells in the meantime. The reliable controller is derived through the ADP approach to make the number of cells achieve the specific ideal states. Firstly, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Secondly, according to nonlinear dynamical mathematical model of tumor cells, chemotherapy and immunotherapeutic drugs can act as powerful regulatory measures, which is a closed-loop control behavior. Finally, states of the system and critic weight errors are proved to be ultimately uniformly bounded with the appropriate optimization control strategy and the simulation results are shown to demonstrate effectiveness of the cybernetics methodology.