Iterative Neural Network Training in MATLAB

Iterative neural network training in MATLAB. Enhances model accuracy by retraining with data rows where predictions exceed a 10% error.

In this MATLAB script, we utilize a sophisticated neural network structure to model complex relationships between multiple input variables and their corresponding outputs. Beginning with a baseline dataset, the neural network is trained iteratively. With each iteration, the model's predictions are validated against a new dataset. Rows where predictions have more than a 10% error are appended to the original dataset. The network is then re-trained, enhancing its accuracy with each successive cycle.

By integrating more neurons across multiple layers, we amplify the network's capability to capture intricate data patterns. This method ensures a robust model that adapts to new data patterns, enhancing its predictive accuracy over multiple iterations. Such iterative approaches, combined with a larger and layered neural network, make it a powerful tool for accurate data interpolation.

This description provides an overview of the MATLAB code's purpose and functionality, highlighting its iterative nature, validation process, and the decision to use multiple neurons and layers for increased accuracy.