title: Theorems and application of local activity of CNN with five state variables and one port.
abstract: Coupled nonlinear dynamical systems have been widely studied recently. However, the dynamical properties of these systems are difficult to deal with. The local activity of cellular neural network (CNN) has provided a powerful tool for studying the emergence of complex patterns in a homogeneous lattice, which is composed of coupled cells. In this paper, the analytical criteria for the local activity in reaction-diffusion CNN with five state variables and one port are presented, which consists of four theorems, including a serial of inequalities involving CNN parameters. These theorems can be used for calculating the bifurcation diagram to determine or analyze the emergence of complex dynamic patterns, such as chaos. As a case study, a reaction-diffusion CNN of hepatitis B Virus (HBV) mutation-selection model is analyzed and simulated, the bifurcation diagram is calculated. Using the diagram, numerical simulations of this CNN model provide reasonable explanations of complex mutant phenomena during therapy. Therefore, it is demonstrated that the local activity of CNN provides a practical tool for the complex dynamics study of some coupled nonlinear systems.
authors.affiliation: State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Xiong
authors.forename: Xisong
authors.initials: X
authors.lastname: Dong
authors.forename: Li
authors.initials: L
authors.lastname: Xie
authors.forename: Thomas
authors.initials: T
authors.lastname: Yang
mesh_terms: D006509:Hepatitis B / Q000821:virology; D006515:Hepatitis B virus / Q000235:genetics; D006801:Humans; D055641:Mathematical Concepts; D008959:Models, Neurological; D009154:Mutation; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012641:Selection, Genetic