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In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs.
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We compare the behavior of these attractor neural networks with empirical data from both physiology and psychology.
A measure of the dynamics of an attractor.
In particular, every trajectory initiated within the bounds of an attractor terminates inside the attractor.
By the definition of an attractor, there is no path among the attractors in a deterministic model.
The probability to differentiate into various attractors in turn represents the robustness of an attractor a i.
To verify the chaotic behavior of many generating attractors, the Maximum Lyapunov Exponent (MLE) is calculated for such systems.
The reduction method presented in this paper is a useful tool for simplifying the mathematical analysis of various behaviors of attractor network models to better understand how these behaviors depend on the network parameters and the learning process.
Unlike conventional multiscroll attractor designs, in which the resulting attractor is composed of symmetric reflections of an original attractor produce by generalizing benchmarks like Chua's circuit.
We develop a new numerical technique and apply it to study the global behavior of the flow on the attractor.
Some basic dynamical properties, such as Lyapunov exponents, fractal dimension, Poincare mapping, the continuous spectrum and chaotic behavior of this new transverse butterfly attractor are studied.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com