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010   $a981-9752-80-9
024 7 $a10.1007/978-981-97-5280-5
035   $a(MiAaPQ)EBC31611751
035   $a(Au-PeEL)EBL31611751
035   $a(CKB)34168611300041
035   $a(DE-He213)978-981-97-5280-5
035   $a(EXLCZ)9934168611300041
100   $a20240823d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvanced Spiking Neural P Systems $eModels and Applications /$fby Hong Peng, Jun Wang
205   $a1st ed. 2024.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024.
215   $a1 online resource (304 pages)
225 1 $aComputational Intelligence Methods and Applications,$x2510-1773
311 08$a981-9752-79-5 
327   $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.
330   $aMembrane computing is a class of distributed and parallel computing models inspired by living cells. Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks. Models and applications constitute two major research topics in spiking neural P systems. The entire book comprises two parts: models and applications. In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems. In the application part, the application of spiking neural P systems in time series prediction, image processing, sentiment analysis, and fault diagnosis is examined. This offers a novel method and model for researchers in artificial intelligence, data mining, image processing, natural language processing, and power systems. Simultaneously, it furnishes engineering and technical personnel in these fields with a powerful, efficient, reliable, and user-friendly set of tools and methods. .
410  0$aComputational Intelligence Methods and Applications,$x2510-1773
606   $aArtificial intelligence
606   $aComputer science
606   $aImage processing
606   $aNatural language processing (Computer science)
606   $aMachine learning
606   $aArtificial Intelligence
606   $aModels of Computation
606   $aTheory of Computation
606   $aImage Processing
606   $aNatural Language Processing (NLP)
606   $aMachine Learning
615  0$aArtificial intelligence.
615  0$aComputer science.
615  0$aImage processing.
615  0$aNatural language processing (Computer science)
615  0$aMachine learning.
615 14$aArtificial Intelligence.
615 24$aModels of Computation.
615 24$aTheory of Computation.
615 24$aImage Processing.
615 24$aNatural Language Processing (NLP).
615 24$aMachine Learning.
676   $a006.3
700   $aPeng$b Hong$01631565
701   $aWang$b Jun$0855712
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910882888003321
996   $aAdvanced Spiking Neural P Systems$94209912
997   $aUNINA