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.