Fractional High Frequency Cosine and Sine Higher Order Neural Network for Economics
Chenrui Hu, Ming Zhang · 2019
The data in the real world are complex and varied. When we apply artificial neural networks to simulate the real-world commerce data through simple functions, accuracy could become problematic. To overcome this issue, we could choose Higher Order Neural Network (HONN) models, which can simulate a data set very accurately. This paper has developed a new open-box HONN model with fractional functions, called a Fractional High Frequency Cosine and Sine HONN (FHFCSHONN) model. FHFCSHONN structure and learning algorithm formulas are studied too. This paper also built a new software package called a FHFCSHONN simulator. Based on the commerce data testing results, the FHFCSHONN model's average error is 0.6980%, while the other three HONN model average errors are 4.0160%, 4.2370%, and 4.3556% respectively.