Improved waste mobile phone pricing method based on fuzzy neural network

Abstract

The invention discloses a fuzzy neural network pricing method based on momentum method parameter updating, and belongs to the field of machine learning and data mining. A fuzzy neural network is usedas a pricing model, and a momentum method is used to update parameters of the model. The method comprises the steps that firstly, key features influencing pricing of the waste mobile phones are selected by a model through adoption of a principal component analysis method; secondly, a waste mobile phone pricing model based on a fuzzy neural network is established, and accurate analysis between mobile phone pricing and key characteristic variables is achieved; and finally, parameters of the fuzzy neural network are optimized by adopting a momentum method to realize parameter updating of the pricing model. According to the invention, the pricing accuracy of the waste mobile phone can be well improved.

Description

technical field [0001] The invention belongs to the field of machine learning and data mining, in particular to a fuzzy neural network pricing method based on momentum method parameter update. Background technique [0002] In recent years, as consumers replace mobile phones more and more frequently, how to dispose of used mobile phones has become a topic of increasing concern. The recycling of used mobile phones can promote a virtuous circle of sustainable economic and environmental development. However, more and more used mobile phones on the market make manual pricing one by one unrealistic. Fortunately, with the development of the Internet and big data technology, data mining technology-based The intelligent pricing mechanism has become a new business model, which is conducive to promoting the formation of a win-win situation for consumers and enterprises. [0003] The current mainstream pricing methods are divided into tangible asset valuation methods in economics, valu...

AI Technical Summary

Problems solved by technology

Since there is a nonlinear mapping relationship in the problem of pricing waste mobile phones, and the curve oscillation of the traditional gradient descent method to find the optimal solution is highly volatile, each update may not follow the correct direction, and it is easy to fall into a local minimum point, which is difficult to solve. Obtaining the global optimal solution will eventually affect the pricing accuracy

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