Course Name: CH5440 : Multivariate Data Analysis for Process Modeling

Description: Use of data analysis for process performance enhancement Mathematical background: Sources and characterization of errors in data, Random variables, probability density functions, estimation, confidence intervals and hypothesis testing, stochastic signals, frequency domain analysis of signals, measures of nongaussianity. Process Modeling: Model structures, linear regression, Nonlinear regression, principla Component Analysis, Independent Component Analysis. Applications: Parameter estimation in linear and nonlinear processes, Data Reconcilation, Continuous/Batch process monitoring using MSPC, controller performance monitoring, fault diagonis, chemometrics, biomedical and speedh signal processing.

Slot: G

RoomNo: MSB356

Instructor: Shankar Narasimhan S

Period: JAN-MAY 2013

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