The improvements of BP neural network learning algorithm

By Wen Jin, Z. Li, L. Wei, Han Zhen. 2000

270 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiangqiang Yuan
2. Chao Zeng
3. H. Eugene Stanley
4. Huanfeng Shen
5. Tongwen Li
6. Liangpei Zhang

In Control and Systems Engineering

1. H. Eugene Stanley

Citations in the corpus

• Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, background): “It better generates complex and non-linear responses than a standard regression analysis [35].”
• Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment (2016, background): “, 2005) have indicated that ANNs can outperform the classic statistical models. However, the well-known BPNN has the disadvantages of slow convergence velocity and easily converging to local minimum (Wen et al., 2000; Yu, 1992). Hence, we introduce here another neural network named the generalized r”
• A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Back propagation neural network (BPNN) The back propagation neural network (BPNN) model is one of the most widely used artificial intelligence algorithms for classification and prediction [52].”