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Abstract: Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting. However, this type of research always determines the parameter settings of the NNs rationally through a trial-and-error methodology. This paper integrates design of experiment (DOE), Taguchi method, and back-propagation NN (BPNN) to construct a robust engine to further optimize the prediction accuracy under a robust DOE-based predictor. Adopting data from Taiwan Stock Exchange (TWSE), the technical analytical indexes and β value of the listed stocks of TWSE were computed. The research results indicated that the proposed approach can effectively improve the forecasting rate of stock price variations. [Copyright &y& Elsevier]

Abstract: This study develops a three-stage integrated approach with some heuristic working rules to assist the planner in generating a best and most effective assembly sequence. In the first stage, Above Graph and transforming rule are used to create a correct explosion graph of the assembly models. In the second stage, a three-level relational model, with geometric constraints and assembly precedence diagrams (APDs), is generated to create a complete relational model graph and an incidence matrix. In the third stage, the back-propagation neural network is employed to optimize the available assembly sequence. Two real-world examples are utilized to evaluate the feasibility of the proposed model in terms of the difference of assembly sequences. The results show that the proposed model can facilitate assembly sequence optimization and allows the designer to recognize the contact relationship and assembly constraints of three-dimensional (3D) components in a virtual environment type. [Copyright &y& Elsevier]