Analyzing on the Failure Mode of BFNNs´ Learning and its Improving Algorithm

In order to improve the learning mechanism of BFNNs, the paper firstly analyzes the failure mode of BFNNs trained by SBALR, which takes the form of a local cycle. And then by mean of the sensitivity theory, a disturbance learning algorithm is developed to make the BFNNs that suffering from learning failure to escape the local cycle. The new algorithm aims to keep the existing learning performance as much as possible. Experimental results demonstrate the effectiveness of the new algorithm on both learning effect and learning efficiency.