ECONOMIC TURNING POINT FORECASTING USING THE FUZZY NEURAL NETWORK AND NON-OVERLAP AREA DISTRIBUTION MEASUREMENT METHOD
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This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by the bounded sum. To simplify the forecasting processes, the non-overlap area distribution measurement method is applied to select important features by deleting less important inputs. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.
Abstract
This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by the bounded sum. To simplify the forecasting processes, the non-overlap area distribution measurement method is applied to select important features by deleting less important inputs. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.
참고문헌 (22)
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