{"id":55135,"date":"2023-08-27T04:09:20","date_gmt":"2023-08-27T04:09:20","guid":{"rendered":"https:\/\/www.inspirenignite.com\/anna-university\/cic333-system-identification-syllabus-for-ice-2021-regulation-professional-elective-iii\/"},"modified":"2023-08-27T04:09:20","modified_gmt":"2023-08-27T04:09:20","slug":"cic333-system-identification-syllabus-for-ice-2021-regulation-professional-elective-iii","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/cic333-system-identification-syllabus-for-ice-2021-regulation-professional-elective-iii\/","title":{"rendered":"CIC333: System Identification syllabus for ICE 2021 regulation (Professional Elective-III)"},"content":{"rendered":"

System Identification detailed syllabus for Instrumentation & Control Engineering (ICE) for 2021 regulation curriculum has been taken from the Anna Universities<\/a> official website and presented for the ICE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below. <\/p>\n

For Instrumentation & Control Engineering 5th Sem scheme and its subjects, do visit ICE 5th Sem 2021 regulation scheme<\/a>. For Professional Elective-III scheme and its subjects refer to ICE Professional Elective-III syllabus scheme<\/a>. The detailed syllabus of system identification is as follows. <\/p>\n

Course Objectives:<\/h4>\n

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Unit I<\/h4>\n

NON PARAMETRIC METHODS
\nNonparametric methods: Transient analysis – frequency analysis – Correlation analysis -Spectral analysis.\n<\/p>\n

Unit II<\/h4>\n

PARAMETRIC METHODS
\nParametric model structures: ARX, ARMAX, OE, BJ models – The Least square estimate -Best linear unbiased estimation under linear constraints – Updating the Parameter estimates for linear regression models – Prediction error methods: Description of Prediction error methods – Optimal Prediction – Relationships between prediction error methods and other identification methods – theoretical analysis. Instrumental variable methods: Description of Instrumental variable methods – Theoretical analysis – covariance matrix of IV estimates -Comparison of optimal IV and prediction error methods.\n<\/p>\n

Unit III<\/h4>\n

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Unit IV<\/h4>\n

CLOSED- LOOP IDENTIFICATION
\nIdentification of systems operating in closed loop: direct identification and indirect identification – Subspace Identification methods: classical and innovation forms – Relay feedback identification of stable processes.\n<\/p>\n

Unit V<\/h4>\n

NONLINEAR SYSTEM IDENTIFICATION
\nModeling of nonlinear systems using ANN- NARX & NARMAX – Training Feed-forward and Recurrent Neural Networks – TSK model – Adaptive Neuro-Fuzzy Inference System (ANFIS) -Introduction to Support Vector Regression.<\/p>\n

Skill Development Activities<\/h4>\n

(Group Seminar\/Mini Project\/Assignment\/Content 10 Preparation \/ Quiz\/ Surprise Test \/ Solving GATE questions\/ etc)<\/p>\n

    \n
  1. Familiarization of various system identification methods in MATLAB.<\/li>\n
  2. Seminar on ANFIS<\/li>\n
  3. Exploration of other advanced system identification methods.<\/li>\n<\/ol>\n

    Course Outcomes:<\/h4>\n

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    Text Books:<\/h4>\n
      \n
    1. Lennart Ljung, \u00ef\u00bf\u00bdSystem Identification: Theory for the user\u00ef\u00bf\u00bd, 2nd Edition, Prentice Hall, 1999.<\/li>\n
    2. Dan Simon, \u00ef\u00bf\u00bdOptimal State Estimation Kalman, H-infinity and Non-linear Approaches\u00ef\u00bf\u00bd, John Wiley and Sons, 2006,<\/li>\n
    3. Tangirala, A.K., \u00ef\u00bf\u00bdPrinciples of System Identification: Theory and Practice\u00ef\u00bf\u00bd, CRC Press, 2014, 1st Edition.<\/li>\n<\/ol>\n

      Reference Books:<\/h4>\n
        \n
      1. Cortes, C., and Vapnik, V., \u00ef\u00bf\u00bdSupport-Vector Networks, Machine Learning\u00ef\u00bf\u00bd, 1995, 1st Edition.<\/li>\n
      2. Miller, W.T., Sutton, R.S., and Webrose, P.J., \u00ef\u00bf\u00bdNeural Networks for Control\u00ef\u00bf\u00bd, MIT Press, 1996, 1st Edition.<\/li>\n
      3. Van der Heijden, F., Duin, R.P.W., De Ridder, D., and Tax, D.M.J., \u00ef\u00bf\u00bdClassification, Parameter Estimation and State Estimation\u00ef\u00bf\u00bd, An Engineering Approach Using MATLAB, John Wiley & Sons Ltd., 2017, 2nd Edition.<\/li>\n
      4. Karel J. Keesman, \u00ef\u00bf\u00bdSystem Identification an Introduction\u00ef\u00bf\u00bd, Springer, 2011, 1st Edition.<\/li>\n
      5. Tao Liu and Furong Gao, \u00ef\u00bf\u00bdIndustrial Process Identification and control design, Step-test and relay-experiment-based methods\u00ef\u00bf\u00bd, Springer- Verlag London Ltd., 2012, 1st Edition.<\/li>\n<\/ol>\n

        List of Open Source Software\/ Learning Website:<\/h4>\n