Abnormal Behavior of Fractional Differential Equations in Processing Computer Big Data

Abstract

We use the Legendre wavelet method to study nonlinear fractional differential equations. Based on the in-depth study of the characteristics of various fractional-order dynamic system models, this paper designs a system for solving fractional-order differential equations, and we apply them to the anomaly analysis of big computer data. This method can improve the efficiency of big data classification. The results of computer numerical simulation show that the designed algorithm for solving fractional differential equations has high accuracy. At the same time, the algorithm can avoid misclassification and omission in big data analysis.

Synchronization in Finite Time of Fractional-Order Complex-Valued Delayed Gene Regulatory Networks

“…As is well known, fruitful results based on the gene network of integer-order differential equations have been reported [17][18][19]. With the development of the theory of fractional-order calculus and fractional-order differential equations [20][21][22][23], various applications of gene regulatory networks that employ fractional-order calculus, such as the fields of medical science, control, and biotechnology, have shown distinct advantages due to the merits of memory and heredity properties, see [24][25][26] and the references therein. In [24], the results indicated that the most significant benefit of using gene regulatory networks with fractional orders for their memorability and hereditary properties is the enhancement in the dexterity and accuracy of models.…”