Load cell has nonlinear error because of different ambient temperature in work

and it is necessary to compensate.The principle of temperature error of load cell is illustrated

and a method for compensating this error based on radial basis function neural network（RBFNN） is proposed.The training algorithm of RBFNN is described.Using this proposed temperature error compensation method and the load cell which the weighing range is 100 kg

the experiments are implemented under 0

6

0 ℃.The results show that temperature errors of load cell are decreased

and its weighing accuracy is increased.