A Novel CMAC Neural Network for Correcting the Sensor''''s Nonlinear Errors
用于传感器非线性误差校正的新颖神经网络

In this paper, the principles and the methods for correcting the nonlinear errors of the sensor system with a neural network are shown, and a novel simplified cerebella model articulation controller (SCMAC), which includes its model, algorithm and realized techniques, is proposed. The direct weight address mapping techniques are used in this model and algorithm, and the relation between the inputs and the weights is established by taking the inputs of training samples as their weight address, and the corresponding weights are found for any inputs by taking it as similar weight address, and the accurate outputs are obtained by the associable insertion algorithm. In addition, the weights are stored and addressed in a magnetic disk file, therefore, the overflow of internal memory of microcomputer are avoided, and the SCMAC is easily realized. Finally, a simulation experiment is given and the results show that the nonlinear errors of the sensor system are decreased to approximate zero after correcting with a SCMAC.