Advanced Spiking Neural P Systems : Models and Applications / / by Hong Peng, Jun Wang

• Record Nr.: UNINA9910882888003321
• Autore: Peng Hong
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• ISBN: 981-9752-80-9
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (304 pages)
• Collana: Computational Intelligence Methods and Applications, , 2510-1773
• Altri autori (Persone): WangJun
• Disciplina: 006.3
• Soggetti: Artificial intelligence; Computer science; Image processing; Natural language processing (Computer science); Machine learning; Artificial Intelligence; Models of Computation; Theory of Computation; Image Processing; Natural Language Processing (NLP); Machine Learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 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.
• Sommario/riassunto: 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. .