Economizing Enhanced Fuzzy Morphological Associative Memory

Enhanced fuzzy morphological associative memory (EFMAM) successfully conquers the common obstacle of MAM and FMAM, i.e. the extreme vulnerability to the hybrid noise. However, as the number of training patterns increases, EFMAM encounters difficulties in hardware realization, because its network architecture becomes larger and larger. Meanwhile its space and time complexity also rapidly increase. The reason consists in the un-economization of empirical kernel map (EKM) vectors in EFMAM. In this paper, we propose an economized EFMAM, called E²FMAM, which first define a criterion to economize EKM vectors, then use the famous genetic algorithms (GAs) to search the optimum. The simulation results show that E²FMAM has less space and time complexity than EFMAM, and a comparable recognition performance to EFMAM in terms of the tolerance to different types and levels of noise or information incompletion. Besides, its insensitivity to image resolution brings us the flexibility in the higher-resolution image recognition problem.