Wine Quality Evaluation Model Based on Artificial Bee Colony and BP Neural Network

Traditional BP neural network has the disadvantage that it is easy to fall into the local optimum, easily get affected by initial value, so the effect is not stable in practical application. Therefore, in this paper, when the BP neural network is used for the evaluation of the quality of red wine, the artificial bee colony algorithm is used to optimize it, the optimal parameters of the artificial neural network algorithm with the best fitness are used to replace the random initialized parameters of the BP neural network, so as to avoid the neural network falling into a local optimum, can solve the problem of slow convergence speed of the neural network algorithm. The experimental results show that this method has higher accuracy and stability.