Virtual Analyzers: Identification Approach

A definition of virtual analyzers as software algorithmic systems generating models in real time on the basis of current and retrospective information about the industrial processes was given. Methods of development of the virtual analyzers were presented, as well as examples of their industrial applications.

[1]  John C. Doyle.  Analysis of Feedback Systems with Structured Uncertainty , 1982 .

[2]  Mathukumalli Vidyasagar,et al.  Optimal rejection of persistent bounded disturbances , 1986 .

[3]  R.K. Jurgen,et al.  Sarnoff Labs: 'still crazy' but coping , 1988, IEEE Spectrum.

[4]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  F. Bloom,et al.  Brain, Mind, and Behavior , 1985 .

[7]  Jonathan L. Shapiro,et al.  Genetic Algorithms in Machine Learning , 2001, Machine Learning and Its Applications.

[8]  Munther A. Dahleh,et al.  Controller design for plants with structured uncertainty , 1993, Autom..

[9]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[10]  R. Hecht-Nielsen,et al.  Neurocomputing: picking the human brain , 1988, IEEE Spectrum.

[11]  Abraham Kandel,et al.  Fuzzy relational data bases : a key to expert systems , 1984 .

[12]  Ian Petersen.  A new extension to Kharitonov's theorem , 1987, 26th IEEE Conference on Decision and Control.

[13]  Antonia J. Jones,et al.  Genetic algorithms and their applications to the design of neural networks , 1993, Neural Computing & Applications.

[14]  B. R. Barmish,et al.  Extreme Point Results for Robust Stability of Interval Plants: Beyond First Order Compensators , 1991 .

[15]  M. Khammash,et al.  Performance robustness of discrete-time systems with structured uncertainty , 1991 .

[16]  A. P. Kurdjukov,et al.  Stochastic approach to H/sub /spl infin//-optimization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[17]  T. Söderström,et al.  Instrumental variable methods for system identification , 1983 .

[18]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[19]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[20]  Sergei Ovchinnikov,et al.  Fuzzy sets and applications , 1987 .

[21]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[22]  Andrew Packard,et al.  The complex structured singular value , 1993, Autom..

[23]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[24]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[25]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[26]  Christopher V. Hollot,et al.  Robust stabilization of interval plants using lead or lag compensators , 1990 .

[27]  J. Doyle,et al.  Quadratic stability with real and complex perturbations , 1990 .

[28]  Frank J. Bartos.  Artificial intelligence: Smart thinking for complex control , 1997 .

[29]  G. Stein,et al.  Beyond singular values and loop shapes , 1991 .

[30]  Ya.Z. Tsypkin.  Stochastic Discrete Systems With Internal Models , 1997 .

[31]  S. Bhattacharyya,et al.  A generalization of Kharitonov's theorem; Robust stability of interval plants , 1989 .

[32]  Peter Cheeseman,et al.  Fuzzy thinking , 1995 .

[33]  Joseph Jastrow,et al.  Mind and behavior , 1929 .

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .