In order to improve the development of gastrointestinal dyskinesis clinical detecting technology, a detecting system of gastrointestinal multiple biological parameters is established and the error compensation for its pressure sensor' s non-linear error is investigated. The basic theory of the system' s spread silicon piezo-resistance absolute pressure sensor is presented and the reason of the sensor' s non-linear error is analyzed. Based on traditional substractive clustering algorithm, an improved substractive-density clustering method is presented to form a RBF neural network for the sensor' s non-linear error compensation. It is used to cluster samples for network' s initial centers. Gradient descent algorithm is applied to train the network. The method' s effectiveness and practicability are proved by the experiment with actual system' s test data. The result indicates that the system's error is corrected into the region from -1 to 4 kPa, which is better than traditional methods. The system is optimized, the performance is improved and the system's practical need is satisfied.%为推动胃肠道动力功能障碍型疾病临床诊查技术的发展,研制了胃肠道多元生理参数无创检测系统,针对该系统中压力传感器的非线性误差补偿问题进行研究.介绍了系统所采用的扩散硅压阻式绝对压力传感器的原理,分析了这类传感器的非线性误差产生原因.在传统的减法聚类算法的基础上,提出基于改进的减法一密度聚类算法的RBF网络的传感器非线性误差补偿方法,对样本数据进行聚类操作,用来确定RBF神经网络的初始聚类中心,并结合梯度下降法对网络参数和权值进行训练.结合实际系统的实验数据进行了方法验证和效果分析.实验结果表明:方法在系统误差纠正方面比传统方法提高至-1～4 kPa,使得测量结果准确性得以较大的提高,满足了系统的应用需求.
展开▼
