基于神经网络的大柔性灵巧手指弯曲角度预测

The bending angle prediction of large flexible dexterous finger based on neural network

介绍了一种新型大柔性灵巧手指,该手指弯曲性能好、易于控制但数学建模复杂.基于BP神经网络基本理论,建立预测该手指弯曲角度的BP神经网络模型.通过大柔性灵巧手指弯曲特性实验获得样本数据,借助于MATLAB仿真软件中的神经网络工具箱作为开发平台,将实验样本数据用于BP网络训练.利用训练好的BP网络模型对手指弯曲角度进行预测,预测误差范围控制在3%以内.研究结果表明:这种神经网络模型能够准确预测手指的弯曲角度.

A new kind of large flexible dexterous finger was presented, the finger has good bending performance and can be controlled easily however with complicated mathematical modeling. Based on BP neural network theory, a BP network model was established to predict the bending angle of the finger. The sample data obtained through the experiment of the finger bending characteristics were used to train BP network using neural network toolbox in MATLAB software. The trained BP network model was used to predict the bending angle of the finger. The prediction error is controlled less than 3%. The result shows the network model can accurately predict the bending angle of the finger.