A Saddle Point Finding Method for Lorenz Attractor through Business Machine Learning Algorithm

Abstract

Cancer has a long time history in our human health experience. Practically, one-fifth of the disease was caused by virus infection. Thus, it is important for us to understand the virus-cancer infection mechanism. Statistically, we may perform the necessary causality regression analysis in such situation to build up the corresponding model just like my previous case in influenza-weather infection. In the present research, I will interaction the systems of differential equations (Lorenz System) with my HKLam theory and figure out the recursive result that we may get. Then, we may go ahead for the corresponding policy generated from the dynamic programming that can solve the Markov decision process. In addition, we may apply the HKLam theory to the chaotic time series and compare the model with machine learning one for a better selection with failure explanation. Finally, I will also discuss a novel mathematical method in determining the saddle point in Lorenz attractor together with the gradient descent. The aim is to find the equilibrium for the Lorenz attractor with given initial conditions. It is hope that the mathematics-statistics interaction together with the causal regression (artificial intelligence) model may finally help us fight against those diseases such as virus-infected cancer or others.