基于加速度信号和进化RBF神经网络的人体行为识别

Human Activity Recognition Based on Acceleration Signal and Evolutionary RBF Neural Network

针对基于加速度信号的人体行为识别,采用递阶遗传算法(HGA)训练径向基函数(RBF)神经网络,获得满意的识别正确率.设计适应度函数,利用四分位数间距改进HGA中参数基因的交叉方式,给出自动确定子代生成区域的方法,省去以往同类算法中的经验性设定,并结合算术交叉选择优秀子代,然后对比均匀变异和非均匀变异子代的适应值,实现对RBF网络结构和参数的联合优化.在基于加速度信号的行为识别系统中,与基本HGA和其他常用的训练方法相比,文中算法训练的RBF分类器可获得更低的输出误差和更高的测试样本识别正确率.

To obtain a satisfactory recognition rate, a radial basis function （RBF） neural network classifier trained by the hierarchy genetic algorithm （HGA） is utilized to classify human body activities using the acceleration signal. By exploring the interquartile range, a fitness function is proposed to enhance the crossover of the parameter genes in HGA and determine the distance between the offspring and the boundary of coding space automatically. Thus, the empirical setting in the previous algorithms is avoided. With the arithmetic crossover, the offspring with high fitness is chosen. By comparing fitness values between the uniform mutation offspring and the non-uniform mutation offspring, the structure and parameters of RBF network are jointly optimized. The experimental results on actual subject testing data indicate that the radial basis function neural network classifier trained by the proposed method produces smaller errors than those trained by the traditional HGA. A higher recognition rate of testing data is obtained.