Abstract
A fast simulation method for time-derivative cellular neural networks (TDCNN) is proposed. Using forward Euler approximation (FEA) for the derivative of the cell state and the backward Euler approximation (BEA) for the derivatives of the neighboring cell states enables the recursive computation of the cell state and provides a speed advantage of orders of magnitude. The state equations are then packed into a vector-matrix form which enables the previously empirically given time constraint to be expressed as a matrix condition. It is shown that using both FEA and BEA leads to a second-order difference equation whose corresponding second-order differential equation is derived and shown to yield the same simulation results.
Keywords
Euler method
Derivative (finance)
Computation
Applied mathematics
Matrix (chemical analysis)
Differential equation
Constraint (computer-aided design)
Artificial neural network
Euler's formula
Time derivative
Mathematics
Finite element method
State vector
Computer science
Algorithm
Mathematical analysis
Physics
Geometry
Chemistry
Subject Areas
Neural Networks Stability and Synchronization
·Computer Networks and Communications ·Physical Sciences
Nonlinear Dynamics and Pattern Formation
·Computer Networks and Communications ·Physical Sciences
stochastic dynamics and bifurcation
·Statistical and Nonlinear Physics ·Physical Sciences
Citations by Year
2013: 1
2014: 1
2015: 1
2018: 1
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