基于PSO-BP神经网络的人体穴位定位系统设计  

Acupoint positioning system based on PSO-BP neural network

作　　者：杨向萍[1] 吴玉丹 Yang Xiangping;Wu Yudan(College of Mechanical Engineering,Donghua University,Shanghai 201600,China)

机构地区：[1]东华大学机械工程学院,上海201600

出　　处：《电子技术应用》2018年第9期75-78,共4页Application of Electronic Technique

摘　　要：穴位的位置是否找准会直接影响治疗效果,因此设计了一种基于粒子群算法优化神经网络(PSO-BP)的穴位相对坐标预测模型,然后与ARM结合构成一个可以用于人体穴位定位的系统。首先采用PC进行MATLAB仿真训练学习,然后将最优权值及阈值保存下来并简化算法嵌入ARM内,将在线预测转变为离线过程。实验结果表明:经粒子群优化过的BP神经网络有效地改善了局部极值缺陷,可应用于定位端预测穴位的位置,并在LCD中显示穴位相关信息,控制端收到位置数据后可执行电机上的运动操作。The location of acupoints will directly affect the therapeutic effect,so we designed a prediction model of relative coordinates based on particle swarm optimization and neural network(PSO-BP),and then combined with ARM to form a system for locating human acupuncture points.Firstly,PC machine is used for MATLAB simulation training and learning.After that,the optimal weights and thresholds are saved,and the algorithm is embedded in ARM,and online prediction is transformed into offline process.The experimental results show that the BP neural network optimized by particle swarm optimization can effectively improve the local extreme defects.It can be applied to locate the location of the acupoints at the location end,and display the information of the points in LCD.After the control terminal receives the location data,it can perform the movement operation on the motor.

关 键 词：穴位坐标 定位 粒子群算法 BP神经网络 ARM 

分 类 号：TN4[电子电信—微电子学与固体电子学]