Backpropagation in Neural Networks and Its Alignment with Universal Laws of Systems

Abstract Backpropagation is a fundamental algorithm in artificial neural networks that allows systems to learn efficiently by minimizing errors through feedback and gradient-based optimization. This paper examines the mechanism of backpropagation and demonstrates its natural alignment with the three universal laws proposed by Malicse: the Law of Karma (systems must be free of defects), the Law of Balance (universal equilibrium), and the Law of Feedback/Interconnectedness. Through mathematical description, practical examples, and analogies to natural and electronic systems, this paper argues that backpropagation is not merely a computational tool but a reflection of universal principles governing system efficiency and balance.