Abstract: Traditionally, the physical flowmeter is used to measure the flow. However, the physical flowmeter is expensive and difficult to repair. Using neural network technology for dynamic flow measurement has the advantage of lower price and easy maintenance, which has important significance in hydraulic technology. The widely used BP algorithm has the problems of easy falling into local minimum point and low convergence speed. In view of the problems, an improved momentum back propagation (BP) algorithm with an odd parameter is proposed. First, a starting weight value is given. Then, the excitation function is improved to change the weight value in order to increase the convergence speed and gradient. At the same time, the number of useless test samples is decreased. The correctness of the proposed algorithm is proved by theory. The test result indicates that the new algorithm can save training time about 9.65% and the training step is also decreased about 31.13% than traditional BP algorithm while satisfying the requirement of convergence and training precision. The proposed algorithm is suitable for real-time dynamic soft flow measurement.

Key words: Convergence, Flow measurement, Momentum BP algorithm, Neural network