摘要
提出一种基于人工免疫神经网络的表面肌电信号模式识别方法.在对表面肌电信号进行预处理的基础上,免疫RBF神经网络模型中抗原集合作为网络的输入数据即为表面肌电信号,抗体为抗原的压缩聚类映射作为径向基函数神经网络模型的隐层中心,则网络的输出为下肢各关节的角度预测值.仿真结果表明,免疫RBF神经网能明显提高对肌电信号的识别准确率,这对于肌电假肢的控制具有良好的应用前景.
Based on artificial immune neural network,this paper proposed a pattern recognition method of surface electromyography.After pretreating surface electromyography signal,the antigen set of immune RBF neural network(surface electromyography signal) is regarded as input data and the antibodies(compression cluster mappings of antigens) are the hidden layer centers of radial basis function neural network model,and angle predictive values of lower limb joints are the network output.Simulation results show that immune REF neural network can obviously improve the recognition accuracy of EMG signals,and has good application prospect in myoelectric prosthesis control.
出处
《河北工业大学学报》
CAS
北大核心
2010年第6期13-16,共4页
Journal of Hebei University of Technology
基金
国家自然科学基金(60575009)
河北省自然科学基金(E2010000053)
关键词
人工免疫
RBF神经网络
肌电信号
模式识别
artificial immune
RBF neural network
electromyography signal
pattern recognition