BAM: A Bidirectional Associative Memory Neural Network Architecture

BAM is a two-layer hetero-associative memory network composed of content-addressable memory units forming a feedback architecture. Main classifications include discrete, continuous, and adaptive BAM variants. The BAM model was originally proposed by Kosko in 1987-1988, featuring bidirectional operation where input signals at one layer generate outputs at the opposite layer, with continuous feedback iterations until network stabilization.

Set-Based Discrete Particle Swarm Optimization Algorithm

Implementation of a set-based discrete particle swarm optimization algorithm with customizable parameters for immediate execution. Key variables include: global popsize (population size), global pop (population matrix), global c1 (cognitive coefficient), global c2 (social coefficient), global gbest_x (global best x-coordinate), and global gbest (global best solution). The algorithm requires proper parameter initialization for optimal performance.