A time simulated annealing-back propagation algorithm and its application in disease prediction

Abstract

In this paper, based on the Back Propagation (BP) neural network algorithm, we introduce the idea of the Simulated Annealing (SA), and then propose a new neural network algorithm: Time Simulated Annealing-Back Propagation (TSA-BP) algorithm. The proposed algorithm can improve the convergence rate and numerical stability. By using this proposed algorithm, the learning rates and initial weights in the BP neural network could be easily adjusted. We show that the TSA-BP algorithm could reduce the errors caused by human-made factors. Several numerical experiments have been tested by using different disease data. Furthermore, we compared the TSA-BP algorithm to the other existing, well-known algorithms. Numerical results show higher accuracy and efficiency of the TSA-BP algorithm.