Telecommunication customer loss forecasting method based on nervous-netowrk improved algorithm

Abstract

The method comprises: firstly using BP neural network to build a client running off model; then using the error propagation factor to make normalization process for the data with a normalization formula: x'=0.8(x-xmin / xmax-xmin)+0.1, and wherein, x is an inputted parameter, xmin is a minimum inputted parameter, xmax is a maximum parameter, x is a normalized inputted parameter.

Description

technical field [0001] The invention belongs to the field of data mining of telecommunication operators, and relates to a method for predicting telecommunication customers (churn) based on an improved neural network algorithm. Background technique [0002] With the development of competition in the telecommunication market, customers have more and more room to choose telecommunication products and telecommunication companies, and the scramble for customers among telecommunication companies is becoming more and more fierce. Facing the increasingly fierce market competition environment, the traditional and passive service system of telecom companies can no longer meet the needs of customers and meet the challenges of competitors. At the same time, it is difficult for traditional advantages such as network and technology to widen the gap among telecom companies, and it is impossible to form a differentiated competitive advantage. Therefore, in order to c...

AI Technical Summary

Problems solved by technology

[0008] 1. The problem of slow convergence speed limits the further application of this method

[0009] 2. The problem of local minima makes the training error unable to decrease and cannot be used to predict telecom customers (churn)

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