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020 _a9789819752805
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024 7 _a10.1007/978-981-97-5280-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
100 1 _aPeng, Hong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_970340
245 1 0 _aAdvanced Spiking Neural P Systems
_h[electronic resource] :
_bModels and Applications /
_cby Hong Peng, Jun Wang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 297 p. 136 illus., 107 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _aChapter 1. Introduction -- Chapter 2. Spiking Neural P Systems and Variants -- Chapter 3. Computational Completeness -- Chapter 4. Fuzzy Spiking Neural P Systems -- Chapter 5.Time Series Forecasting -- Chapter 6. Image Processing -- Chapter 7. Sentiment Analysis -- Chapter 8. Fault Diagnosis.
520 _aMembrane computing is a class of distributed and parallel computing models inspired by living cells. Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks. Models and applications constitute two major research topics in spiking neural P systems. The entire book comprises two parts: models and applications. In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems. In the application part, the application of spiking neural P systems in time series prediction, image processing, sentiment analysis, and fault diagnosis is examined. This offers a novel method and model for researchers in artificial intelligence, data mining, image processing, natural language processing, and power systems. Simultaneously, it furnishes engineering and technical personnel in these fields with a powerful, efficient, reliable, and user-friendly set of tools and methods. .
650 0 _aArtificial intelligence.
_958441
650 0 _aComputer science.
_958552
650 0 _aImage processing.
650 0 _aNatural language processing (Computer science).
_965131
650 0 _aMachine learning.
_955059
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aModels of Computation.
_966444
650 2 4 _aTheory of Computation.
_967552
650 2 4 _aImage Processing.
650 2 4 _aNatural Language Processing (NLP).
_966164
650 2 4 _aMachine Learning.
_955059
700 1 _aWang, Jun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_971684
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819752799
776 0 8 _iPrinted edition:
_z9789819752812
776 0 8 _iPrinted edition:
_z9789819752829
830 0 _aComputational Intelligence Methods and Applications,
_x2510-1773
_970583
856 4 0 _uhttps://doi.org/10.1007/978-981-97-5280-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
999 _c62679
_d62679