rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Fuzzy and Neuro-Fuzzy Intelligent Systems
  name:
    name part: Czogala, Ernest.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Leski, Jacek.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2000
    edition: 1st ed. 2000.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 195 p. 72 illus. online resource.
  abstract: Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: • diagnosing diseases, • localizing faults in electronic circuits, • optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
  table of contents: 1 Classical sets and fuzzy sets Basic definitions and terminology -- 1.1 Classical sets -- 1.2 Fuzzy sets -- 1.3 Operations on fuzzy sets -- 1.4 Classification of t-norms and t-conorms -- 1.5 De Morgan triple and other properties of t- and s-norms -- 1.6 Parameterized t-, s-norms and negations -- 1.7 Fuzzy relations -- 1.8 Cylindrical extension and projection of fuzzy sets -- 1.9 Extension principle -- 1.10 Linguistic variable -- 1.11 Summary -- Bibliographical notes -- 2 Approximate reasoning -- 2.1 Interpretation of fuzzy conditional statement -- 2.2 An approach to axiomatic definition of fuzzy implication -- 2.3 Compositional rule of inference -- 2.4 Fuzzy reasoning -- 2.5 Canonical fuzzy if-then rule -- 2.6 Aggregation operation -- 2.7 Approximate reasoning using a fuzzy rule base -- 2.8 Approximate reasoning with singletons -- 2.9 Fuzzifiers and defuzzifiers -- 2.10 Equivalence of approximate reasoning results using different interpretations of if-then rules -- 2.11 Numerical results -- 2.12 Summary -- Bibliographical notes -- 3 Artificial neural networks -- 3.1 Introduction -- 3.2 Artificial neural networks topologies -- 3.3 Learning in artificial neural networks -- 3.4 Back-propagation learning rule -- 3.5 Modifications of the classic back-propagation method -- 3.6 Optimization methods in neural networks learning -- 3.7 Networks with output linearly depending on parameters -- 3.8 Global optimization methods -- 3.9 Summary -- Bibliographical notes -- 4 Unsupervised learning Clustering methods -- 4.1 Introduction -- 4.2 Self-organizing feature map -- 4.3 Vector quantization and learning vector quantization -- 4.4 An overview of clustering methods -- 4.5 Fuzzy clustering methods -- 4.6 A possibilistic approach to clustering -- 4.7 New generalized weighted conditional fuzzy c-means -- 4.8 Fuzzy learning vector quantization -- 4.9 Cluster validity -- 4.10 Summary -- Bibliographical notes -- 5 Fuzzy systems -- 5.1 Introduction -- 5.2 The Mamdani fuzzy systems -- 5.…
  note: by Ernest Czogala, Jacek Leski.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Information technology
  subject:
    topic: Business-Data processing
  subject:
    topic: Artificial Intelligence
  subject:
    topic: IT in Business
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Fuzziness and Soft Computing, 47
  identifier: 9783790818536
  identifier: https://doi.org/10.1007/978-3-7908-1853-6
  record info:
    record creation date: 121227
    record change date: 20191025031818.0
    record identifier: 978-3-7908-1853-6