Cascade-Based Non-Linear Feedforward Neural Networks for Bi-Directional Memory

In today's world, efficient computation is the key to success in many fields. Pattern association plays an influential role areas of life, such as learning and memory. complex dynamics, bidirectional associative memory has been effectively demonstrated by neural networks. However, these networks face challenges terms performance, computational time. A random bipolar input pattern output a matrix with different sizes were used analyze background this study. order address memory, nonlinear considered be most feasible method. study, we present cascade-based non-linear feedforward network that performs two passes behaves like Bayesian algorithm. English alphabets patterns have validate results approach. Using experimental results, study evaluated BAM's equivalent association, stability.