|
| 摘要: |
| 通过对标准BP算法的改进，提出了一种L-M贝叶斯正则化优化算法，并把它应用到成都市居民消费水平预测中。经试验验证，L-M贝叶斯正则化的BP神经网络比相同条件下另外两种改进算法有更强的泛化能力，对居民消费水平有很好的预测效果。 |
| 关键词: BP神经网络 L-M优化算法 贝叶斯正则化算法 居民消费水平 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| Application of a Kind of Improved BP Algorithm to Consumption Level |
|
SONG Feng
|
| Abstract: |
| Through improving standard BP algorithm,this paper proposes a kind of L-M Bayesian regularization optimization algorithm and applies this algorithm to the prediction of Chengdu resident consumption level.Experiment shows that BP neural network of L-M Bayesian regulaarization hais stronger generalization than another two kinds of improved algorithms under the same condition and has better forecasting effect on resident consumption level. |
| Key words: BP neural netwok L-M optimization algorithm Bayesian regularization algorithm resident consumption level |
