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000 nam5i
001 2210080934773
003 DE-He213
005 20250321105433
007 cr nn 008mamaa
008 240823s2024 si | s |||| 0|eng d
020 a97898197528059978-981-97-5280-5
024 a10.1007/978-981-97-5280-52doi
040 a221008
050 aQ334-342
050 aTA347.A78
072 aUYQ2bicssc
072 aCOM0040002bisacsh
072 aUYQ2thema
082 a006.3223
100 aPeng, Hong.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
245 00 aAdvanced Spiking Neural P Systemsh[electronic resource] :bModels and Applications /cby Hong Peng, Jun Wang.
250 a1st ed. 2024.
264 aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.
300 aXIV, 297 p. 136 illus., 107 illus. in color.bonline resource.
336 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
347 atext filebPDF2rda
490 aComputational Intelligence Methods and Applications,x2510-1773
505 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 aArtificial intelligence.
650 aComputer science.
650 aImage processing.
650 aNatural language processing (Computer science).
650 aMachine learning.
650 aArtificial Intelligence.
650 aModels of Computation.
650 aTheory of Computation.
650 aImage Processing.
650 aNatural Language Processing (NLP).
650 aMachine Learning.
700 1 aWang, Jun.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut
710 aSpringerLink (Online service)
773 tSpringer Nature eBook
776 iPrinted edition:z9789819752799
776 iPrinted edition:z9789819752812
776 iPrinted edition:z9789819752829
830 aComputational Intelligence Methods and Applications,x2510-1773
856 uhttps://doi.org/10.1007/978-981-97-5280-5
912 aZDB-2-SCS
912 aZDB-2-SXCS
950 aComputer Science (SpringerNature-11645)
950 aComputer Science (R0) (SpringerNature-43710)
Advanced Spiking Neural P Systems[electronic resource] :Models and Applications /by Hong Peng, Jun Wang
Material type
전자책
Title
Advanced Spiking Neural P Systems[electronic resource] :Models and Applications /by Hong Peng, Jun Wang
Author's Name
Wang Jun. author
판 사항
1st ed. 2024.
Physical Description
XIV, 297 p 136 illus, 107 illus in color online resource.
Keyword
Membrane 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. .
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