Predictive Analytics using Neuro Fuzzy Model and error estimation for a Dynamic Process Control
Keywords:
Data prediction, ANFIS, PID control, Membership functions, PCA, LSEAbstract
Empirical data collected from a real-time process controlled by a Proportional Integral Derivative controller is non-linear due to the nature of the control action and also embarked with signal noise and process disturbances that influence the control objective. A predictive model built using such data set doesn’t converge due to the presence of redundancies and outliers. Hence the data collected is statistically treated to generate data sets from empirical data by eliminating the uncertainties thus adhering with the first principles. In this paper Neuro fuzzy modeling is endeavored with both the empirical data and the pretreated data and the model convergence is studied using various membership functions and training strategies. It is observed that errors are predominant with the predictive model using the empirical data while statistical treatment renders models with closer affinity. Data from different experiments are analyzed. Maximum error of 0.78 % is seen with empirical data while and the model converges with training error of 0.002 % for data reconstructed using PCA.
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