Sales Forecast Of An Automobile Industry (Page 4 of 4) in pdf

International Journal of Computer Applications (0975 – 8887)

Volume 53– No.12, September 2012

IEEE’97 page no.1381-1386Tavel, P. 2007 Modeling

Table 2. Comparison of Minimum and Maximum values

of both models

and Simulation Design. AK Peters Ltd.

[4] Jingtao Yao, Nicholas Teng, Hean-Lee Poh , Chew Lim

Tan(1998) Forecasting and Analysis of Marketing Data

Maximum

Minimu

Using Neural Networks Journal Of Information Science

Range

Value

m value

And Engineering 14, 843-862.

[5] Stoeva

Stefka, Nikov

Alexander(2000) .A fuzzy

backpropagation algorithm. Fuzzy Sets and Systems 112

Neural Network

-3

8.3X10

(2000) 27-39.

Model

[6] Gholamreza

Jandaghi,

Reza

Tehrani,

Davoud

100%

Multiple

Hosseinpour, Rahmatollah Gholipour and Seyed Amir

confidenc

0.3611

-0.3626

0.7237

Linear

Shahidi Shadkam ,Application of Fuzzy-neural networks

Regressi

in multi-ahead forecast of stock price African Journal of

50%

Business Management Vol. 4(6), pp. 903-914, June 2010

Confidenc

0.2079

-0.1956

0.4035

Model

[7] Yan Liu, Min Sun Fuzzy Optimization BP Neural

Network Model for Pavement Performance Assessment

Proceedings of 2007 IEEE International Conference on

3. CONCLUSION

Grey Systems and Intelligent Services, November 18-20,

The present work has successfully implemented sales forecast

2007, Nanjing, China.

model of an automobile industry using Fuzzy BPN algorithm.

[8] Jason E. Kutsurelis, Forecasting Financial Markets Using

The result obtained by neural network is further compared

Neural Networks: An analysis of methods and accuracy,

with the result obtained by multiple linear regression analysis.

Thesis September 1998 , Naval Postgraduate School

The result obtained by multiple linear regressions have range

Monterey, California.

of 0.7237 for 100% confidence and 0.4935 for 50%

confidence. The result range obtained by proposed algorithm

[9] Rajasekaran

S,Vijayalakshmi

G.A.,”Neural

-3

is of order 8.3X10

and is found to be superior to the result

Networks,Fuzzy Logic and Genetic Algorithms”,PHI,

obtained by multiple linear regressions. The error obtained by

2003.

neuro-fuzzy BP architecture is 7.7728e-006.

[10] Little, John D. C., Decision Support Systems for

Marketing Managers, Journal of Marketing (pre-1986);

4. REFERENCES

Summer 1979; 43, 000003; ABI/INFORM Global pg. 9.

[1] Chang Pei-Chann , Wang Yen-Wen (2006). Fuzzy

Delphi and back propagation model for sales forecasting

[11] Horris C. bung, (1995) Neural Network in supply chain,

in PCB

industry Expert Systems with Applications 30

Engineering Management-Conference, IEEE, pg 347-

(2006) 715–726.

352.

[2] Mirbagheri Mirnaser(2010). Fuzzy-logic and Neural

[12] A. Weigend and N. Gerschenfeld, Eds., Time Series

network Fuzzy forecasting of Iran GDP growth. African

Prediction: Forecasting the future and Understanding the

past reading, MA: Addision –Wesley, 1994.

Journal of Business Management Vol. 4(6), pp. 925-929.

[3] Escoda , A.Ortega, ASanz , A.Herms (1997) Demand

Forecasting by neural fuzzy technique IEEE, FUZZ-

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