English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

BP neural network is a common kind of neural network for recognition and classification, but it is obvious that some difficulties are find during the training process. In this paper, we introduced the basic principle of BP algorithm and propose a parameter adjustment procedure for activation function to speed up convergence and avoid gradient diffusion, and the feasibility of this method is proved by a basic BP neural network with simple structure. We analyzed this parameter's available value through a group of experiments based on simple three-layer BP full connected network. In addition, this activation function parameter adjustment procedure is also used in is suitable for Multilayer BP neural networks. Keywords—neural network; gradient diffusion; activation function; convergence speed; classification accuracy

Research on the Solution of BP Neural Network Training Problem