Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception

Abstract

The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image that is noisy. The "inverse" of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. To our knowledge, there is no solution to this in the literature other than for an oversimplified model in which the true image is garbled with noise by the perceiver himself. In this paper, we propose a chaotic model of pattern recognition (PR) for the theory of "blurring." This paper, which is an extension to a companion paper demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the companion paper in which a chaotic PR system extracts the pattern from the input, in this case, we show that even without the inclusion of additional noise, perception of an object can be "blurred" if the dynamics of the chaotic system are modified. We thus propose a formal model and present an analysis using the Lyapunov exponents and the Routh-Hurwitz criterion. We also demonstrate experimentally the validity of our model by using a numeral data set. A byproduct of this model is the theoretical possibility of desynchronization of the periodic behavior of the brain (as a chaotic system), rendering us the possibility of predicting, controlling, and annulling epileptic behavior.

An Enhanced Tree-Shaped Adachi-Like Chaotic Neural Network Requiring Linear-Time Computations

“…The AdNN's power as a PR system requires excessive computationsof the order of O(N 2 ). For the examples cited by Adachi et al and Calitoiu et al [1][2][3][4][5][6][7], which use 10 × 10 pixel arrays, this involves 10, 000 computations per time step. 2.…”

“…This paper deals with the Adachi Neural Network AdNN [1][2][3][4][5], which possesses a spectrum of very interesting chaotic, AM and PR properties, as described in [6,7]. The fundamental "problem" associated with the AdNN and its variants is its quadratic computational requirement.…”

“…The Adachi Neural Network (AdNN) [1][2][3][4][5], is a fascinating Neural Network (NN) which has been shown to possess chaotic properties, and to also demonstrate Associative Memory (AM) and Pattern Recognition (PR) characteristics. Variants of the AdNN [6,7] have also been used to obtain other PR phenomena, and even blurring. A significant problem associated with the AdNN and its variants, is that all of them require a quadratic number of computations.…”

Secure Communications via Stochastic Constrained Uncertain Hyperchaotic System

“…Most of the chaos-based communications schemes are based on chaos synchronization between transmitter and receiver [3]- [11]. In that regard, the techniques used for chaos synchronization can be classified into control techniques [3]- [6], estimation techniques such as extended Kalman filter (EKF) [2], [7]- [9], and artificial intelligent approaches [10] among others. In these approaches, masking the contents of a message using chaotic signals have been achieved using different methods [7], [10].…”

“…In that regard, the techniques used for chaos synchronization can be classified into control techniques [3]- [6], estimation techniques such as extended Kalman filter (EKF) [2], [7]- [9], and artificial intelligent approaches [10] among others. In these approaches, masking the contents of a message using chaotic signals have been achieved using different methods [7], [10]. However, it has been shown that most of these techniques are not secure since it is possible to extract the encoded message signal from the transmitted chaotic signal by using different unmasking techniques [12], [13].…”