Abstract
In this paper, according to the requirements of clothing sales forecast, the forecast model of clothing sales is constructed. Through the design of cyclic structure, the adverse effects of uncertainty, hysteresis, and time-varying factors of the predicted object are overcome, and the prediction procedure is theoretically standardized. Aiming at the shortcomings of traditional NN sales forecasting algorithm, such as low learning efficiency, slow convergence speed, and easy to fall into local minimum, this paper puts forward some improvement measures. Adaptive learning efficiency is used to improve the effectiveness and convergence of the algorithm, additional momentum method is used to improve the adaptability of the algorithm, and improved GA is used to optimize the weights of NN. Improve the global optimization characteristics of GA to achieve the purpose of fast optimization and accurate prediction. Finally, an example is used to verify the algorithm. On this basis, the correlation adaptability and prediction accuracy of clothing prediction methods are compared and analyzed, combined with the theoretical analysis of various methods, to explore the practical applicability of various methods under different prediction conditions. It provides an important basis for the decision-making of garment enterprises.
Highlights
China’s textile and garment industry is one of the industries that can best reflect international competitiveness
A sales forecast network model is established based on an analysis of the factors that influence clothing sales, and Genetic algorithm (GA) is used to optimize the calculation of each connection weight of the back propagation neural network (BPNN)
With the help of the characteristics of GA, it can effectively avoid the shortcomings of BP network and combine the advantages of the two algorithms to improve the prediction accuracy and convergence speed when it is applied to the modeling and prediction process of BPNN
Summary
China’s textile and garment industry is one of the industries that can best reflect international competitiveness. The research of NN has made progress and achievements in many aspects, proposed a large number of network models, found many learning algorithms, and successfully discussed and analyzed the system theory of NN [8]. On this basis, the artificial neural network (ANN) has achieved fruitful applications in the fields of pattern classification, machine vision, machine hearing, robot control, signal processing, combinatorial optimization problem solving, associative memory, coding theory, medical diagnosis, financial decision-making, data mining, and so on [9]. The feasibility and accuracy of the genetic BP network are verified by simulation experiments It can be used in the sales forecast of garment enterprises
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