基于神经网络算法的工业机器人控制方法研究

Research on Control Method of Industrial Robot Based on Neural Network Algorithm

随着人工智能技术的快速发展,神经网络算法被广泛应用于工业机器人的控制策略中,以提高其智能化水平和操作效率.神经网络,尤其是深度学习模型,因其出色的非线性映射能力和自学习特性,逐渐成为提升机器人性能的关键技术.文中详细探讨了神经网络算法在工业机器人控制中的应用,包括神经网络的结构设计、训练与优化方法、实时控制策略的实现以及故障排除和调试,旨在展示如何通过神经网络提高机器人的操作精确度和响应速度,并通过系统测试验证所提方法的有效性与实用性.

With the rapid development of artificial intelligence technology,neural network algorithms have been widely applied in the control strategies of industrial robots to improve their intelligence level and operational efficiency.Neural networks,especially deep learning models,have gradually become key technologies for improving robot performance due to their excellent nonlinear mapping ability and self-learning characteristics.This paper explores in detail the application of neural network algorithms in industrial robot control,including the structural design,training and optimization methods of neural networks,implementation of real-time control strategies,and troubleshooting and debugging.The aim is to demonstrate how to improve the operational accuracy and response speed of robots through neural networks,and to verify the effectiveness and practicality of the proposed method through system testing.