Titlebook: Cellular Neural Networks: Dynamics and Modelling; Angela Slavova Book 2003 Springer Science+Business Media Dordrecht 2003 information proc

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K. W. Rothe,H. Walther,J. Wernerhe CNN equations describing reaction-diffusion systems are with the large number of cells, they can exhibit new phenomena that can not be obtained from their limiting PDEs. This demonstrates that an autonomous CNN is in some sense more general than its associated nonlinear PDE.

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K. W. Rothe,H. Walther,J. Wernertem whose state is characterized by two scalar variables . and . and we shall assume that they depend continuously on time .. They will play the role of independent and dependent variables, respectively. In the terminology of CNN, they are also named input and output, or also control and state, respectively.

Sleep-Paralysis

Dynamics of nonlinear and delay CNNs, this chapter we shall show that by retaining all the important features of the original CNN structure [27] and by introducing very simple nonlinear and delay templates, the CNN becomes a good framework for general analogue array dynamics.

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Hysteresis and Chaos in CNNs,r. Moreover, because of the applications of CNN, it will be interesting to consider a special type of memorybased relation between an input signal and an output signal in this circuit. The main goal of this chapter is to model and investigate such relation, called hysteresis [154] for a CNN.

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Mathematical Modelling: Theory and Applicationshttp://image.papertrans.cn/c/image/223051.jpg

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Appendix A. Topological degree method,In this appendix we shall define the degree of a map in .. and derive some useful properties [34,81,93].