A Prediction Method of Soybean Moisture Content in the Process of Soy Sauce Brewing Production Using Quantum Revolving Gate of Quantum Evolution Algorithm Back Propagation

Abstract

Soybean fermentation is a key of production of soy sauce, and directly affects its quality. In order to solve the problem of slow convergence speed and low error precision in VLBP, a new model of optimized VLBP by quantum revolving door of quantum evolution algorithm has been proposed. The model and algorithm are performed the quantum-behaved data into input-layer of VLBP which is transformed by the principle of phase shift in the quantum behavior theory, and the weigh data is performed a controlled-NOT gate as the output-layer input to reform the forward transmission. With the steepest descent method, the information is transferred by the error back propagation perform updating the weighs in the hidden-layer and output-layer by involving the controlled-NOT gate’s transfer function. Finally, the trained algorithm has been used for forecasting the soybean moisture content of soaking time and temperature using the R-QEABP and VLBP neural network algorithm. Compared with VLBP, the results have been indicated that the R-QEABP has great advantages of convergence speed, lower verify error, and better robustness for the purpose of realizing the accurate prediction of soybean moisture content in the process of soy sauce brewing production.