A fuzzy model for the representation and recognition of linguistically described trends

Below we present the Fuzzy Temporal Profile model (FTP) for the representation and recognition of patterns in the evolution of a physical parameter. One of the fundamental aspects of the model is the linguistic acquisition of the information that constitutes the profile. Thus, this proposal deals with a fuzzy representation of the semantics associated to the linguistic description that is made by experts on different forms of evolution of a physical parameter, and the manner in which this representation is used in pattern recognition tasks.

[1]  Henry A. Kautz,et al.  Constraint Propagation Algorithms for Temporal Reasoning , 1986, AAAI.

[2]  D. Dubois,et al.  Systems of linear fuzzy constraints , 1980 .

[3]  Roque Marín,et al.  Modeling the representation of time from a fuzzy perspective , 1994 .

[4]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[5]  James C. Bezdek,et al.  Curvature and Tangential Deflection of Discrete Arcs: A Theory Based on the Commutator of Scatter Matrix Pairs and Its Application to Vertex Detection in Planar Shape Data , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  D. Qian.  Representation and use of imprecise temporal knowledge in dynamic systems , 1992 .

[7]  Richard W. Jones,et al.  Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns , 1999, Artif. Intell. Medicine.

[8]  Paulo Félix Lamas.  Perfiles temporales borrosos: un modelo para la representación y reconocimiento de patrones sobre señal , 1999 .

[9]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[10]  Bhavik R. Bakshi,et al.  Representation of process trends—IV. Induction of real-time patterns from operating data for diagnosis and supervisory control , 1994 .

[11]  Roland T. Chin,et al.  On the Detection of Dominant Points on Digital Curves , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Barbara Hayes-Roth,et al.  tFPR: A fuzzy and structural pattern recognition system of multi-variate time-dependent pattern classes based on sigmoidal functions , 1998, Fuzzy Sets Syst..

[13]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[14]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[15]  H von Specht,et al.  Fuzzy sets for feature identification in biomedical signals with self-assessment of reliability: an adaptable algorithm modeling human procedure in BAEP analysis. , 1995, Computers and biomedical research, an international journal.

[16]  Martin A. Fischler,et al.  Locating Perceptually Salient Points on Planar Curves , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Eugene C. Freuder.  A Sufficient Condition for Backtrack-Free Search , 1982, JACM.

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[19]  Ugo Montanari,et al.  Networks of constraints: Fundamental properties and applications to picture processing , 1974, Inf. Sci..

[20]  Rina Dechter,et al.  Temporal Constraint Networks , 1989, Artif. Intell..

[21]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[22]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[23]  Panos A. Ligomenides.  Real-time capture of experiential knowledge , 1988, IEEE Trans. Syst. Man Cybern..

[24]  Ira J. Haimowitz,et al.  Clinical monitoring using regression-based trend templates , 1995, Artif. Intell. Medicine.

[25]  Alan K. Mackworth.  Consistency in Networks of Relations , 1977, Artif. Intell..

[26]  Madan M. Gupta,et al.  Introduction to Fuzzy Arithmetic , 1991 .

[27]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[28]  Theodosios Pavlidis,et al.  Algorithms for Shape Analysis of Contours and Waveforms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Yoav Shoham,et al.  Temporal Logics in AI: Semantical and Ontological Considerations , 1987, Artif. Intell..

[30]  Friedrich Steimann.  The interpretation of time-varying data with DIAMON-1 , 1996, Artif. Intell. Medicine.

[31]  Mohan S. Kankanhalli.  An adaptive dominant point detection algorithm for digital curves , 1993, Pattern Recognit. Lett..

[32]  Robert J. Schalkoff,et al.  Pattern recognition : statistical, structural and neural approaches / Robert J. Schalkoff , 1992 .

[33]  G. Stephanopoulos,et al.  Representation of process trends—Part I. A formal representation framework , 1990 .

[34]  Caroline M. Eastman,et al.  Response: Introduction to fuzzy arithmetic: Theory and applications : Arnold Kaufmann and Madan M. Gupta, Van Nostrand Reinhold, New York, 1985 , 1987, Int. J. Approx. Reason..

[35]  Didier Dubois,et al.  Processing fuzzy temporal knowledge , 1989, IEEE Trans. Syst. Man Cybern..

[36]  Rina Dechter,et al.  Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition , 1990, Artif. Intell..

[37]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .

[38]  Senén Barro,et al.  A model and a language for the fuzzy representation and handling of time , 1994 .

[39]  Senén Barro,et al.  Linguistic Representation of Fuzzy Temporal Profiles , 1999, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

[41]  L. Zadeh,et al.  Fuzzy sets and applications : selected papers , 1987 .