Optimization of Diagonal Recurrent Neural Networks Using Genetic Algorithm, Particle Swarm Optimization, and BP Algorithm

This article discusses three optimization algorithms implemented in MATLAB for enhancing Diagonal Recurrent Neural Networks (DRNN). The methods include Genetic Algorithm (GA) for global parameter optimization through selection, crossover, and mutation operations; Particle Swarm Optimization (PSO) for weight tuning via particle velocity and position updates; and Backpropagation (BP) algorithm for gradient-based local optimization. These techniques improve DRNN performance in terms of accuracy and computational efficiency. The MATLAB implementations demonstrate key functions such as population initialization for GA, fitness evaluation for PSO, and gradient descent calculations for BP, accompanied by practical code examples. We further analyze each algorithm's advantages (e.g., GA's global search capability) and limitations (e.g., BP's sensitivity to initial weights), along with their applications in fields like time-series prediction and system identification. This guide provides comprehensive insights into DRNN optimization with executable MATLAB frameworks.