Integration of novel uncertainty models construction of college art and art design teaching system based on big data analysis

Abstract

In this paper to explore the improvement direction of college art and art design teaching system, this paper combines big data technology and intelligent data processing model to construct college art and art design system, and proposes a new blind equalization algorithm of online echo state network based on prediction principle. Moreover, this paper adopts the complex ESN as the prediction error filter, and adds the phase rotation factor after the amplitude adjustment, which solves the limitation that the existing blind equalization algorithm based on the prediction principle cannot handle complex signals. In addition, this paper uses the Kalman filter algorithm to update the parameters of the network online to achieve the purpose of tracking the channel changes in real time. The experimental research results show that the college art and art design teaching system proposed in this paper has a good teaching effect and can effectively improve the effect of art and art design education in colleges and universities.