Standard Elman Network Implementation with PSO Algorithm Optimization

This implementation combines a standard Elman neural network with a Particle Swarm Optimization (PSO) algorithm to optimize the weights of the Elman network. The code generates visual outputs and features straightforward, easy-to-understand implementation. The program includes comprehensive data preprocessing modules that handle normalization and feature scaling, along with result visualization components that plot training progress and performance metrics. Additionally, it incorporates an automatic hyperparameter tuning functionality that dynamically adjusts parameters based on user requirements to achieve optimal model performance. The implementation utilizes key MATLAB functions such as 'train' for network training and custom PSO functions for weight optimization. The Elman network architecture maintains context units for temporal dependencies, while the PSO algorithm implements velocity and position updates using social and cognitive components. Overall, this program not only provides fundamental Elman neural network capabilities but also offers enhanced functionality and convenience, enabling users to perform neural network modeling and optimization with greater efficiency.