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Dynamic Nested Optimization with Adaptive Memory Control for Continual Learning
Deep learning models have made great progress in many areas, but they are limited by their fixed structure and single-step optimization process, which makes it hard for them to adjust to changing data over time. This paper presents a Dynamic Nested Optimization Framework (DNOF) based on the idea of nested learning, where learning is organized as a layered system of connected optimization steps that work across different time frames and contexts. Unlike standard methods that use just one type of gradient update, this framework uses multiple layers of optimization. This lets the system learn model settings, optimization techniques, and memory structures at the same time. The layered design helps the system understand both short-term and long-term patterns, making the learning process more stable and the model more accurate. The model was tested on a structured dataset and performed very well, achieving an accuracy of 98.986%. These results show that the model learns efficiently and remains stable. The findings show that nested optimization improves prediction accuracy and makes training more reliable. This research helps move machine learning forward by connecting traditional deep learning methods with adaptable smart systems. It provides a flexible base for ongoing learning and future AI models.
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