Abstract
Cost prediction is very important for cost control, but the factors of influencing cost are many and complex. The factors affect each other, and the coupling phenomenon exists, so enterprise cost is difficult to be predicted correctly. On the basis of production cost composition model, the product cost prediction model based on neural network is established. A hybrid algorithm that trains neural network weight by real-coded adaptive mutation genetic algorithm is designed, and it overcomes the disadvantage that traditional neural network is easy to fall into local minima. Furthermore, the model is successfully applied to cost prediction in an ore dressing plant, and it improves the prediction accuracy.
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, X., Xue, J., Dong, L. (2005). The Modeling and Application of Cost Predication Based on Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_149
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DOI: https://doi.org/10.1007/11427469_149
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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