Self-Adaptive Electronics using Artificial Neural Networks

Electronic gadgets and systems are growing rapidly, requiring new adaptability methods. This research uses Artificial Neural Networks to self-adapt electronic systems. ANNs, inspired by the brain, can optimize electronic circuits and devices in real time.Self-adaptive electronics uses ANNs for system control and decision-making. ANNs learn to adapt to changing operational circumstances, environmental factors, and user preferences through supervised and reinforcement learning. Feedback systems let neural networks improve energy usage, system parameters, and performance without operator interaction.A sensor array, artificial neural network-based control unit, and actuators make up the self-adaptive electronics system. The system smoothly integrates sensors and computing. Self-adaptive electronics could be used in loT, wearable, and autonomous systems, the research says. Electronic systems operate in unpredictable and dynamic contexts, thus the architecture is adaptable.Experimentally, self-adaptive electronics outperform static systems in performance, energy efficiency, and adaptability. Artificial neural networks can enable smarter, more responsive, and autonomous gadgets, according to the findings. Finally, self-adapting artificial neural networks in electronics could lead to intelligent systems. This research advances adaptive electronics, enabling new applications in the ever-changing electronics and technology environment.