Algorithm for Applying CMAC Neural Network in Artificial Neural Networks to Robotic Arm Control

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Research on Algorithm for Applying CMAC Network in Artificial Neural Networks to Robotic Arm Control - Featuring Implementation Approaches and Key Function Descriptions

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This research aims to explore the application algorithm of CMAC (Cerebellar Model Articulation Controller) network in artificial neural networks for robotic arm control. Through in-depth study and analysis of the CMAC network architecture, we investigate its implementation in robotic arm control systems to achieve more precise, stable, and flexible movements. The research focuses on algorithm design and optimization, ensuring the robotic arm can accurately execute required tasks across various scenarios. Key implementation aspects include weight update mechanisms using gradient descent methods, hash coding techniques for memory addressing, and real-time control parameter adjustments. The study will provide valuable references and guidance for the development and application of robotic technology, particularly in areas requiring adaptive learning capabilities and nonlinear system control.

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