A robust cellular associative memory for pattern recognitions using composite trigonometric chaotic neuron models - Wimol San-Um (Songklanakarin Journal of Science and Technology (SJST), 2015)
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.