A Predictive Dynamic Neural Network Model Based on Principal Component Analysis (PCA) and its Application

We propose a real-time prediction method based on PCA and improved multi-step Elman net. The method not only preserves most original data information, but also eliminates the relativity among data and simplifies the net structure. It can predict the complex and nonlinear systems with dynamic recurrent algorithm. Through training the net has the ability of adapting to the uncertainty of the nonlinear structure, and then reflects the dynamic character of the systems. The hit rate reaches 88.17% to forecast the silicon content in hot metal of a blast furnace with errors ranging from-0.05 percent to 0.05 percent. The results prove that this method is feasible to predict silicon content on blast furnace and also it’s a prediction method of nice future.