Improving Back Propagation of Feed-Forward Neural Network with Changing Sigmoid Functions
نویسندگان
چکیده
Our study is about feed-forward neural network’s learning method. Generally, the method of improving its learning is focused on learning rate and moment term. We focus on sigmoid functions. Sigmoid functions are used for converting input signal into output signal and adjusting connection weight of learning in feed-forward neural network. We change gradient of sigmoid functions and investigate our method’s effect.
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