Abstract
An enhancement to the growth curve approach based on neuro evolution is proposed to develop various forecasting models to investigate the state and worth of the producer, to market a new product. The forecasting model is obtained using a newly introduced neuro evolutionary approach called Cartesian Genetic Programming based ANN (CGPANN). CGPANN helps in obtaining an optimum model for all the necessary parameters of an ANN. An accurate and computationally efficient model is obtained, achieving an accuracy as high as 93.37% on the time devised terrains, providing a general mechanism for forecasting models in mathematical agreement to its application in econometrics. Comparison with other contemporary model evidences the perfection of the proposed model thus its vital power in developing the growth curve approach for predicting the sustainability of new products.
Chapter PDF
References
Ben-Akiva, M., Bolduc, D.: Multinomial probit with a logit kernel and a general parametetric specipication of the covariance structure. Working paper, Department of Economics. MIT (1996)
Brownstonel, D., Train, K.: Forecasting new product penetration with flexible substitution patterns. Journal of Econometrics, 109–129
Cardell, N., Dunbar, F.: Measuring the societal impacts of automobile downsizing. Transportation Research 14(5,6), 423–434
Dean, J.: Demand forecasting for a new product, http://www.entranceguruji.in/read_matirial.php
Fletcher, D., Goss, E.: Forecasting with neural networks and application using bankruptcy data. Information and Management 24, 159–167 (1993)
Francis, E.H., Cao, L.: Modified support vector machines in financial time series forecasting. Neurocomputing 48, 847–861 (2002)
Hasanat, A.: Object class recognition using neat-evolved artificial neural network. In: Fifth International Conference on Computer Graphics, Imaging and Visualization, CGIV, pp. 271–275 (2008)
Cao, J., Tay, L., Support, H.: vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14(6), 1506–1518 (2003)
Kaastra, I., Milton, S.: Forecasting futures trading volume using neural networks. J. Futures Markets 15, 853–970 (1995)
Kempf, K.G., Keskinocak, P.: Uzsoy: Planning production and inventories in the extended enterprise. International Series in Operations Research and Management Science 152(2), 588–589 (2011)
Khan, G.M., Khan, S., Ullah, F.: Short-term daily peak load forecasting using fast learning neural network. In: Intelligent Systems Design and Applications (ISDA), pp. 843–848 (2011)
Lo, C.-Y.: Back propagation neural network on the forecasting system of sea food material demand. In: Zhou, M., Tan, H. (eds.) CSE 2011, Part II. CCIS, vol. 202, pp. 147–154. Springer, Heidelberg (2011)
McFadden, D.: Conditional logit analysis of qualitative choice behavior. Frontiers in econometrics. Academic Press, New York (1973)
Min, J.H., Lee, Y.C.: Bankruptcy prediction using support vector machinewith optimal choice of kernel function parameters 48, 847–861 (2002)
Petrovic, D., Duenas, A.: A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets and Systems 157(16), 2273–2285 (2006)
Revelt, D., Train, K.: Mixed logit with repeated choices: Households choices of appliance effciency level. Review of Economics and Statistics 80(4) (1998)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Yao, X., Islam, M.M.: Evolving artificial neural network ensembles. IEEE Computational Intelligence Magazine 3(1), 31–42 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ali, J., Khan, G.M., Mahmud, S.A. (2014). Enhancing Growth Curve Approach Using CGPANN for Predicting the Sustainability of New Food Products. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2014. IFIP Advances in Information and Communication Technology, vol 436. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44654-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-662-44654-6_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44653-9
Online ISBN: 978-3-662-44654-6
eBook Packages: Computer ScienceComputer Science (R0)