LOGISTIC MAP NEURAL MODELS
Athanasios I. Margaris, Μanos Roumeliotis · 2002
This paper examines the ability of neural based structures to model the logistic equation. This modelling includes not only the gener- ation of the logistic curve, but also the time series that are generated by the logistic neural model. This study concerns all main regions of the lo- gistic equation: the region of convergence for parameter values less than 3, the periodic region for parameter values in the interval (3, 3.57), and the chaotic region for values in the interval (3.57, 4). For each region, the flxed points of the logistic map are calculated and compared to the cor- responding theoretical points, followed by an analysis of the distribution of the absolute mean error between the theoretical and the experimental curves. Finally, the Lyapunov exponent and the fractal dimensions for both the theoretical and the neural based attractor are estimated.