This theory defines a generative feedback mechanism that restructures internal architectures in response to input variation. It enhances learning adaptability and dynamic performance control by embedding structural self-modification.
本理論は、入力変化に応じて内部構造を再構成する生成型フィードバック機構を定義します。構造の自己修正を組み込むことで、学習適応性や動的な性能制御を向上させます。
This model is applicable in evolving neural networks, reinforcement learning environments, and autonomous adaptive control systems where structural agility and feedback precision are critical.
In daily technologies, it can be used in adaptive UI systems, personalized recommendation engines, and workflow automation tools that self-tune their internal logic and responses.