Optimizing Neural Network Architectures for Autonomous Systems

Abstract

The research focuses on optimizing neural network architectures to improve the decision-making capabilities of autonomous systems. Through a series of simulations and real-world tests, we identify key architectural modifications that enhance efficiency and accuracy. The findings indicate that specific architecture designs can significantly impact system performance, leading to more reliable and intelligent autonomous applications. This study provides a framework for future research in neural adaptive systems.

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