Self-Healing Neural Networks: A path towards Resilient and Autonomous AI Systems

ABSTRACT Self-Healing Neural Networks (SHNNs) represent an advanced approach in Artificial Intelligence designed to improve system resilience and autonomy. Traditional neural networks, though highly accurate, are vulnerable to hardware faults, adversarial attacks, and data drift in mission-critical environments. The proposed SHNN framework integrates fault detection, adaptive retraining, redundancy, and meta-learning to enable automatic recovery without human intervention. By continuously monitoring internal activations and performance metrics, the system identifies degradation and applies corrective measures such as selective retraining and weight reallocation. Experimental results show that SHNNs maintain high accuracy and system uptime even under fault conditions, enhancing overall reliability and robustness. Keywords: Self-Healing Neural Networks (SHNN), Resilient AI, Fault Detection, Adaptive Learning, Meta-Learning, Autonomous Recovery, Neural Network Robustness, Sustainable AI Systems.