Abstract: Traditionally, the physical flowmeter is used to measure the flow. However, the physical flowmeter is not only expensive but also is difficult to repair. Using neural network technology for dynamic flow measurement has the advantages of lower prices and easy maintenance, thereby having important significance in hydraulic technology. Low efficiency of algorithm and easily falling into local minimum point is the mainly problem in present dynamic soft flow measurement. In view of this problem, an improved co-evolutionary genetic and BP algorithm is proposed. Genetic algorithms can overcome the problem of easily falling into local minimum point, and BP algorithm has the advantage of finding out the local minimum point quickly, so the genetic algorithm mixed with BP algorithm is adopted and named co-evolutionary genetic algorithm (CGA)-BP. The algorithm makes a rational structure setting of the stocks, real number coding is adopted, and the reciprocal of network training error is taken as the fitness function. Through adopting the collaborative thinking, making choice, variations, crossing and substitution with generation gap among stocks strengthens the competition between two stocks, thereby promoting the production of more superior stocks. Without affecting the precision of training, it reduces the number of training samples scientifically, thus improving the training speed and reducing training time. Theoretical demonstration shows the feasibility of the algorithm. Its performance is verified through test. Test results show that the algorithm can overcome the problem of easily falling into local minimum point and save time by 9.03% compared to the traditional genetic algorithm. It can better cater for the need of dynamic soft flow measurement.

Key words: BP algorithm, Co-evolutionary genetic algorithm, Flow measurement, Neural network