In this paper, a supervised parallel approach called CMAC (Cerebellar Model Arithmetic
Computer) neural network with an Annealed Chaotic Learning (CMAC-ACL) scheme is proposed to
characters recognition. The CMAC has many advantages in terms of speed of operation based on LMS training, its ability to realize arbitrary nonlinear mapping, and a fast practical hardware implementation.
The CMAC can rapidly obtain output using a nonlinear mapping with look-up table memory to replace the complex learning process with mathematic functions. Additionally, an annealed chaotic learning scheme was embedded to escape from local minima and approach the global minimal solution. The proposed CMAC-ACL was applied to the character recognition in this paper. In the experimental results, the proposed CMAC-ACL has shown that it can clearly distinguish 94 characters in a keyboard
with a size of 8?8 pixels, even though some noise pixels are added in a character.
Asian Journal of Health and Information Sciences 2(1-4):66-78