2007 International Conference on Machine Learning and Cybernetics, 2007
... WEN-CHIN CHEN1, GONG-LOUNG FU2, 3, PEI-HAO TAI2, WEI-JAW DENG4, YANG-CHIH FAN2 ... Lau presen... more ... WEN-CHIN CHEN1, GONG-LOUNG FU2, 3, PEI-HAO TAI2, WEI-JAW DENG4, YANG-CHIH FAN2 ... Lau presented the application of ANNs in suggesting the change of molding parameters for improving dimensional quality (length) of ...
Stock price variation predictions are at the core of many research issues, and neural networks (N... more 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) and
Stock price variation predictions are at the core of many research issues, and neural networks (N... more 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) and
2007 International Conference on Machine Learning and Cybernetics, 2007
... WEN-CHIN CHEN1, GONG-LOUNG FU2, 3, PEI-HAO TAI2, WEI-JAW DENG4, YANG-CHIH FAN2 ... Lau presen... more ... WEN-CHIN CHEN1, GONG-LOUNG FU2, 3, PEI-HAO TAI2, WEI-JAW DENG4, YANG-CHIH FAN2 ... Lau presented the application of ANNs in suggesting the change of molding parameters for improving dimensional quality (length) of ...
Stock price variation predictions are at the core of many research issues, and neural networks (N... more 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) and
Stock price variation predictions are at the core of many research issues, and neural networks (N... more 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) and
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