The result looks rather like the cracks that form in dried mud, in damaged asphalt. Therefore, it is demonstrated that the local activity of CNN provides a practical tool for the complex dynamics study of some coupled nonlinear systems. This lattice pattern fractures and bends as the elephants grow, creating millions of channels across their skin. Using the diagram, numerical simulations of this CNN model provide reasonable explanations of complex mutant phenomena during therapy. 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. The identification problem for spatiotemporal patterns which are generated by autonomous Cellular Neural Networks (CNN) is investigated in this paper. These theorems can be used for calculating the bifurcation diagram to determine or analyze the emergence of complex dynamic patterns, such as chaos. Supports Ubuntu Linux 16.04, Windows 10 and 7. Provide inputs from TensorFlow, Caffe or Keras. Compile Neural Networks developed in common development frameworks, such as TensorFlow, Caffe or Keras, for implementation onto Lattice CNN and compact CNN Accelerator IP cores. 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. Rapidly Compile Networks for Implementation on Lattice sensAI IP Cores. 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. However, the dynamical properties of these systems are difficult to deal with. Coupled nonlinear dynamical systems have been widely studied recently.
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