1 July 1992 Nonstationary and asymmetric net for real-time pattern recognition in noisy environments
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After a discussion of some theoretical limitations and their experimental demonstration of multilayer architectures in contextual pattern recognition, we propose an implementation of a spin-glass like neural net designed to deal efficiently in real time with time-dependent inputs (pattern translations, rotations, scaling, deformations) in noisy environments. The basic idea is a double dynamic on activations and weights on the same time scale. The two dynamics are correlated through an STM locking function on the object. This locking is the means by which the LTM module of the net can perform an invariant recognition of the object under transformations. This is possible owing to the invariant extraction of global features. The net is non-stationary and asymmetrical, because it is able to choose the right correlation order regarding the memorized prototypes for a successful recognition. Nevertheless, the same non- stationary condition, depending on the locking on an object under transformations, implies that the net displays a non-relaxing stabilization. It is presented as an application of the model to the classical recognition problem of rotation `T' and `C' pattern sequences in different noisy contexts.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gianfranco Basti, Antonio Luigi Perrone, Eros Pasero, "Nonstationary and asymmetric net for real-time pattern recognition in noisy environments", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140080; https://doi.org/10.1117/12.140080

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