Research on Improved Shallow Neural Network Big Data Processing Model based on Gaussian Function

The application of the current new generation communication technology is gradually diversified, and the global Internet users are increasing, leading some large enterprises to increasingly rely on faster and more efficient big data processing technology. In order to solve the shortcomings of the current big data processing algorithms, such as slow computing speed, computing accuracy to be improved, and poor online real-time learning ability, this research combines incremental learning and sliding window ideas to design two improved radial basis function (RBF) neural network algorithms with Gaussian function as the kernel function. The Duffing equation example and the data of "Top 100 single products for Taobao search glasses sales" were used to verify the performance of the design algorithm. The experimental results of Duffing equation example show that when the total sample is 100000, the mean square errors of IOL, SWOL, SVM and ResNet50 algorithms are 1.86e-07, 1.59e-07, 3.37e-07 and 2.67e-07 respectively. The experimental results of the data set of "Top 100 SKUs for Taobao Search Glasses Sales" show that when the number of samples in the test set is 800, the root mean square errors of IOL, SWOL, SVM and ResNet50 algorithms are 0.0060, 0.0056, 0.0069 and 0.0073 respectively. This shows that the RBF online learning algorithm designed in this study, which integrates sliding windows, has a stronger comprehensive ability to process big data, and has certain application value for improving the accuracy of online data based commodity recommendation in e-commerce and other industries.

@article{Fu2022,
  title     = {Research on Improved Shallow Neural Network Big Data Processing Model based on Gaussian Function},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {10},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Lifang Fu},
  doi       = {10.14569/IJACSA.2022.0131066},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131066}
}