Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming

doi:10.1109/TCYB.2022.3179302

Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming

2023-03-01

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DOI：10.1109/TCYB.2022.3179302

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This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (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. First, the main objective is to weaken the negative effect caused by chemotherapy and immunotherapy, which means that the minimal dose of chemotherapeutic and immunotherapeutic drugs can be operational in the treatment process. Second, according to the 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 the effectiveness of the cybernetics methodology.

Keywords:

Immune system

Tumors

Chemotherapy

Drugs

Mathematical models

Medical treatment

Regulation

Adaptive dynamic programming (ADP)

chemotherapy and immunotherapy

neural networks

optimal regulation

tumor and immune cells

Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming

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