Using Particle Swarm Optimization to Train Neural Network Parameters

This program utilizes Particle Swarm Optimization (PSO) to train neural network parameters. The implementation features a main controller function that initializes particle positions and velocities, representing potential weight matrices for the neural network. Each particle's fitness is evaluated using forward propagation through the neural network architecture with mean squared error calculation. By executing demoPSOnet.m, you'll observe a dynamic 2D visualization where particle movements correspond to the PSO algorithm's velocity update equations and social-cognitive components driving parameter optimization. The graphical interface displays real-time updates of particle positions (potential solutions) and the global best fitness value, allowing users to monitor convergence behavior and solution quality improvement throughout training cycles. Launch demoPSOnet.m to begin exploring how PSO efficiently navigates high-dimensional parameter spaces to optimize neural network performance!