## 6 Conclusions  
This study has demonstrated the efficacy of NNs for two critical aspects of WPT system optimization: distance prediction and phase angle minimization through frequency adjustment. A key contribution of this work lies in the development of a compact NN architecture for distance prediction, designed specifically for efficient computation and enhanced prediction accuracy. By minimizing the NN's size, overfitting is mitigated, and unnecessary complexity is avoided, enabling potential deployment on a microcontroller. A compact NN structure was also successfully applied to predict the capacitance values required for optimal AIMN configurations. The high prediction accuracy achieved on both simulated and experimental datasets underscores the effectiveness of this approach. Furthermore, this study introduces a novel cascaded NN architecture, combining distance and capacitance prediction. The first NN predicts distance based on impedance values, while the second NN leverages the predicted distance and impedance values to determine the optimal capacitance settings for the AIMN. This cascaded approach not only improves system accuracy but also provides the added benefit of explicit distance estimation. A separate NN was developed to predict the optimal frequency for minimizing the phase angle. Despite the presence of noise and errors in the dataset, this NN achieved excellent results, with errors consistently below 3%. This suggests that NNs can effectively replace or enhance traditional PI control methods for this task. Future studies on noise robustness can be conducted by adding noise to the simulated data and seeing how the accuracy varies with the noise threshold. Critically, the NN-based control offers significant advantages in terms of reduced computational cost and increased speed, requiring only a single measurement compared with the approximately 200 measurements typically needed for conventional control systems. This efficiency makes real-time applications and enhanced WPT power efficiency achievable. While this study primarily presents findings from offline neuralnetwork training and validation, the compact NN architectures (specifically designed with 10-20 neurons) were created for microcontroller deployment and future online adaptation. Given the low network complexity and noise tolerance demonstrated in simulations (MSE \(\leq\) 0.199, tolerance to \(\pm\)15 degrees phase errors), we anticipate similar performance to the offline validation results in real-time scenarios. Microcontroller-specific latency benchmarks and detailed online performance analysis will be included in future work once hardware integration is complete. The application of machine learning techniques to WPT systems, as demonstrated in this study, opens up new possibilities for WPT system automation. Illustrative examples include adaptive EV charging stations that optimize coil positioning for maximum efficiency and WPT systems capable of real-time impedance matching adjustments, even under varying load and misalignment conditions, through microcontroller-based capacitance control. These advancements pave the way for more intelligent and adaptable wireless power solutions.  
Conceptualization, L.S., D.M., F.C. and F.R.F.; methodology, L.S., D.M., F.C. and F.R.F.; validation, L.S., D.M., F.C. and F.R.F.; writing--original draft preparation, L.S., D.M., F.C. and F.R.F.; writing--review and editing, L.S., D.M., F.C. and F.R.F.; supervision, F.C and F.R.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.