neural activation function

基本解释

Method: Based on dynamic error correction BP artificial neural network with Hyperbolic Tangent function as activation function and the number of hidden node six.
方法：利用動態學習比率BP算法以雙曲正切函數爲功能函數的非線性時間序列預測方法。
Methods Based on dynamic error correction BP using artificial neural network with Hyperbolic Tangent function as activation function and the number of hidden node six.
方法利用動態學習比率BP算法以雙曲正切函數爲功能函數的非線性時間序列預測方法。
BP algorithm is often used to correct weights of neural network because number of hidden nodes, studying speed and generation ability of neural network are related to activation function.
以往的BP算法調節神經元網絡的權值，其網絡的隱層結點數、網絡學習快慢程度及網絡的泛化能力都與網絡的激勵函數有關的。