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Application of Combinatorial Predict Method

in Enterprise Profit

MA Dan, ZHOU Sheng-wu

School of Science, China University of Mining and Technology, Xuzhou 221116, China

E-mail:madan518@126.com

Abstract

Seeking for a scientific and reliable enterprise profit predict method is the key point to ensure

the enterprise to get a scientific management. After getting the two predict sequences by

stochastic partial elasticity theory and BP neural network, a new method is established to

solve the combinatorial weighted coefficients by advance the predict accuracy to get a new

predict value. Based on many examples, the new combinatorial predict method is proved to

be very effective and much more accurate, and it can improve the reliability of enterprise

profit prediction.

Keywords: reliability, stochastic partial elasticity theory, BP neural network, predict

accuracy, weighted coefficient.

1 Introduction

Profit is the fundamental purpose of business, the enterprise profit reflect the comprehensive

index of business results and the quality of the management, and also, it is an important measure to

assess situation of enterprise financial and size of economic benefits, as a result, it is a rather important

part

[1-2]

to provide a reliable enterprise profit predict method for scientific management of enterprises.

In business activities, there are so many factors

[3]

impact on the profit, some factors are stochastic,

which can’t be determined in advance. What’s more, various factors do not exist independently, but

interrelated and mutual impact. Therefore it needs to be considered a number of factors at the same

time on the change of the impact on profit.

In the aspect of profit prediction, we often use stochastic partial elasticity theory

[4-5]

to analyze

product demand, supply, interest and income. In the same time, stochastic partial elasticity theory can

also provide effective and reliable theoretical to the decision-makers. Meanwhile, with the randomness

of profit, factors are complex and interrelated, we can create a model between profit changes and

factors, such as BP neural network model

[6]

Stochastic partial elasticity theory is reliable when the product sales and sales prices are

continuous random variable profit on sales volume and prices of stochastic partial elasticity, which can

give various factors affecting the profit of joint probability density. Stochastic partial elasticity

comprehensively reflects the stochastic changes in the scope of elasticity in profit and effectively

control the risk of the business. BP neural network model can simplify the complex impact of external

factors, considering only about the historical data and data model with the internal changes of time, to

identify some training in behavioural change the law from a large number of historical data, then to

describe and explain the whole system in order to predict future state. However, the impact on

enterprise profit targets and the range of factors are very complex, thus profit predict is a complex

system. If a single prediction method was used to simulate, predict and control the structure of the

system input and output, it can only show a part of the system. If both stochastic partial elasticity

theory and neural networks are used, and what’s more they are combinated appropriately and

 

1



Sponsored by the National Natural Science Foundation (10671205).

http://www.paper.edu.cn

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