Artificial Neural Networks in Finance and Manufacturing by Joarder Kamruzzaman, Rezaul K. Begg, Ruhul A. Sarker PDF


By Joarder Kamruzzaman, Rezaul K. Begg, Ruhul A. Sarker

ISBN-10: 1591406706

ISBN-13: 9781591406709

ISBN-10: 1591406714

ISBN-13: 9781591406716

ISBN-10: 1591406722

ISBN-13: 9781591406723

Of crucial elements contributing to nationwide and foreign financial system are processing of knowledge for exact monetary forecasting and selection making in addition to processing of knowledge for effective regulate of producing structures for elevated productiveness. The linked difficulties are very advanced and traditional tools usually fail to supply appropriate suggestions. additionally, companies and industries continuously search for stronger strategies to spice up profitability and productiveness. in recent years, man made neural networks have verified promising ends up in fixing many real-world difficulties in those domain names, and those suggestions are more and more gaining enterprise and recognition one of the practitioners.Artificial Neural Networks in Finance and production offers many cutting-edge and numerous functions to finance and production, in addition to underlying neural community theories and architectures. It bargains researchers and practitioners the chance to entry interesting and state-of-the-art examine concentrating on neural community functions, combining points of financial area in one and consolidated quantity.

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Extra resources for Artificial Neural Networks in Finance and Manufacturing

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Journal of Quality in Maintenance Engineering, 6(2), 86-105. Knapp, G. , & Wang, H. P. (1992). Machine fault classification. A neural network approach. International Journal of Production Research, 30(4), 811-823. Kohonen, T. (1988). Self-organisation and associative memory. New York: SpringerVerlag. Kong, L. , & Nahavandi, S. (2002). On-line tool condition monitoring and control system in forging processes. Journal of Materials Processing Technology, 125126, 464-470. Kruschke, J. K (1989). Improving generalization in backpropagation networks with distributed bottlenecks.

The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

A bibliography of neural network business applications research: 1994-1998. Computer & Operation Research, 27, 1045-1076. Wong, S. , & Hamouda, A. M. S. (2003). Machinability data representation with artificial neural network. Journal of Materials Processing Technology, 138, 538-544. Wu, B. (1992). An introduction to neural networks and their applications in manufacturing. Journal of Intelligent Manufacturing, 3, 391-403. , & Wang, D. (2000). Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

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Artificial Neural Networks in Finance and Manufacturing by Joarder Kamruzzaman, Rezaul K. Begg, Ruhul A. Sarker

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