Download 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

Of an important components contributing to nationwide and overseas economic climate are processing of data for exact monetary forecasting and determination making in addition to processing of knowledge for effective keep an eye on of producing structures for elevated productiveness. The linked difficulties are very advanced and traditional equipment frequently fail to supply applicable recommendations. furthermore, companies and industries regularly search for more suitable options to spice up profitability and productiveness. lately, man made neural networks have validated promising ends up in fixing many real-world difficulties in those domain names, and those innovations are more and more gaining enterprise and recognition one of the practitioners.Artificial Neural Networks in Finance and production provides many cutting-edge and numerous functions to finance and production, in addition to underlying neural community theories and architectures. It deals researchers and practitioners the chance to entry interesting and state of the art examine concentrating on neural community functions, combining facets 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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International Journal of Intelligent Automation and Soft Computing, 9(4), 259-267. Looney, C. G. (1996). Advances in feedforward neural networks: Demystifying knowledge acquiring black boxes. IEEE Transactions on Knowledge & Data Engineering, 8, 211-226. Lowe, D. (1995). Radial basis function networks. In M. A. ), The Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press. MacKay, D. J. C. (1992). Bayesian interpolation. Neural Computation, 4, 415-447. , & Soda, G. (2005). Artificial neural networks for document analysis and recognition.

Our chromosome is a class that contains one matrix Ω and one vector ρ. The matrix Ω is of dimension (I + O) x (H + O). , (H + O -1) is the output unit ( j – H). This representation has the following two characteristics that we are not using in the current version but can easily be incorporated in the algorithm for future work: 1. It allows direct connection from each input to each output units (we allow more than a single output unit in our representation). 2. It allows recurrent connections between the output units and themselves.

The analytical module OPTIS selects the optimum cutting conditions from commercial databases with respect to minimum machining costs. By selection of optimum cutting conditions, it is possible to reach a favorable ratio between the low machining costs and high productivity taking into account the given limitation of the cutting process. Experimental results show that the proposed optimization algorithm for solving the nonlinear constrained programming (NCP) problems is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.

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