CAJ | 학술논문

提出了两种高预测效率、高泛化能力的数值计算方法,一种方法是小波神经网络（Wavelet NeuralNetwork,WNN）算法;另一种是基于小波尺度函数的WSK-SV（Wavelet Scaling Kernel-Support Vector）算法.WNN算法将小波函数与BP神经网络方法相结合,通过输入层、隐含层、输出层间的连接权重以及隐含层使用的激励函数构成了这种算法的关键技术;WSK-SV算法将小波的尺度函数与SV（Support Vector）方法相结合,使这种算法既保持了SVM（Support Vector Machine）的优点,又具有很好的泛化能力.上述两种算法都属于计算智能（Computational Intelligence,简称CI）方法并用于人机系统的性能预测.
제출료량충고예측효솔、고범화능력적수치계산방법,일충방법시소파신경망락（Wavelet NeuralNetwork,WNN）산법;령일충시기우소파척도함수적WSK-SV（Wavelet Scaling Kernel-Support Vector）산법.WNN산법장소파함수여BP신경망락방법상결합,통과수입층、은함층、수출층간적련접권중이급은함층사용적격려함수구성료저충산법적관건기술;WSK-SV산법장소파적척도함수여SV（Support Vector）방법상결합,사저충산법기보지료SVM（Support Vector Machine）적우점,우구유흔호적범화능력.상술량충산법도속우계산지능（Computational Intelligence,간칭CI）방법병용우인궤계통적성능예측.
Two numerical computational methods with high forecast efficiency and high generalization ability are introduced,one being the Wavelet Neural Network(WNN) method and the other the Wavelet Scaling Kernel-Support Vector(WSK-SV) method.The WNN method combines the wavelet function and the BP Neural Network method.The key technique consists of link-weight of the input layer,the hidden layer,the output layer,and the excitation function of the hidden layer.On the other hand,the WSK-SV method does the scaling function and the Support Vector(SV) method with the advantage of Support Vector Machine(SVM) as well as good generalization ability.Both methods above are included in Computational Intelligence(CI) method.They are both applied in performance forecast of human-machine system.