Application of Artificial Neural Networks to Project Cost Analysis for Water Treatment System

The evolvement of semiconductor manufacturing technology indirectly promotes the development of relative industry chain. The investment amount is large; however the product life cycle shows relatively short and it takes nearly 18 months from factory construction to mass production. Based on MATLAB, this thesis constructs Back-propagation neural network(BPNN) to predict the construction cost of water treatment system of semiconductor factories. The reference collected from a third party construction company specialized in water treatment system from 2005 to 2015 were taken for 20 cases including domestic and foreign semiconductor factory bids and cases. The quality of raw water is inputted and system demands the quality of outflow and system category and cost prediction of construction is outputted. Results reveal that a quick, accurate and effective cost prediction could be achieved by Back-propagation neural network(BPNN) and its accuracy is about 93.72% to 99.65% which means the Back-propagation neural network(BPNN) prediction model through this thesis could be a useful reference for following calculations.