Advanced Signal Processing Models in Smart Home Systems

In order to regulate the water level and temperature in contemporary smart home systems, this paper suggests an enhanced control paradigm. The suggested strategy, which integrates the following components—adaptive backbone, Kalman filter, dynamically adjustable PID regulator, and neuro-fuzzy control system—aims to increase efficiency and accuracy in real-time mode. regulation of the system's working characteristics, such as fluctuating water temperatures, changing external circumstances, and a steady reaction to signal noise. The mathematical underpinnings of each component are clarified in detail in this work, along with all the algorithmic solutions needed for their interlineation. A real-time adaptation of a PID regulator, the neuro-fuzzy system, signal smoothing and noise suppression using Kalman filters, and interpolation and extrapolation using a backbone are used to create the mathematical model. The Method model's ability to eradicate mistakes connected to management has been evaluated. The benefits of applying different mathematical techniques are demonstrated and presented via graphs and tables based on the findings gained. Furthermore, a comparison of the model with contemporary rival technology is provided, along with suggested fixes and outcomes to increase its effectiveness. The study's findings will enhance temperature and water level management systems in contemporary smart home systems and increase opportunities for resource efficiency and energy conservation.