A robust cellular associative memory for pattern recognitions using composite trigonometric chaotic neuron models
This paper presents a robust cellular associative memory for pattern recognitions using composite trigonometric chaotic neuron models. Robust chaotic neurons are designed through a scan of positive Lyapunov Exponent (LE) bifurcation structures, which indicate the quantitative measure of chaoticity...
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| Format: | Article |
| Language: | English |
| Published: |
Prince of Songkla University
2015-12-01
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| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | http://rdo.psu.ac.th/sjstweb/journal/37-6/37-6-9.pdf |
| Summary: | This paper presents a robust cellular associative memory for pattern recognitions using composite trigonometric
chaotic neuron models. Robust chaotic neurons are designed through a scan of positive Lyapunov Exponent (LE) bifurcation
structures, which indicate the quantitative measure of chaoticity for one-dimensional discrete-time dynamical systems.
The proposed chaotic neuron model is a composite of sine and cosine chaotic maps, which are independent from the output
activation function. Dynamics behaviors are demonstrated through bifurcation diagrams and LE-based bifurcation structures.
An application to associative memories of binary patterns in Cellular Neural Networks (CNN) topology is demonstrated using
a signum output activation function. Examples of English alphabets are stored using symmetric auto-associative matrix of
n-binary patterns. Simulation results have demonstrated that the cellular neural network can quickly and effectively restore
the distorted pattern to expected information. |
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| ISSN: | 0125-3395 |