(redirected from Tangent Hyperbolic)
TANHTangent Hyperbolic (trigonometry)
References in periodicals archive ?
In this study, the neural network training function of the t sigmoid functions and tangent hyperbolic in the MLP, the acceptable application performance in similar processes Is used and the results are compared To determine the best number of hidden layer neurons is required The root mean square error of network output Count the number of neurons for each hidden layer neurons is selected in a drawing graphs Root mean square error is the lowest number of neurons as the number of neurons in the hidden layer is selected To determine the number of hidden layers in a network similar to this should be done.
Here is the question raised is which of the five models in the best performance will determine the daily discharge To answer this question with the two input patterns, sigmoid functions and tangent hyperbolic is evaluated .
What the tables (2) and (3) can be concluded that the model trained with the first input pattern with 14 provinces sigmoid for the input parameters (1-5-14) means 14 input neurons, 5 hidden neurons and Way that the results of the model trained with a threshold function tangent hyperbolic Has created a better and less error, the predicted peak discharge operation is successful.
five input pattern is introduced, the two patterns in both the, sigmoid functions and tangent hyperbolic, provided acceptable results
The first pattern in both the sigmoid functions and tangent hyperbolic less error than the other four patterns of established and most successful model was among the five models
Sigmoid the results of the tangent hyperbolic with higher correlation coefficient and the RMSE is less