Advanced Spiking Neural P Systems: Models and Applications (Computational Intelligence Methods and Applications) - Tapa dura

Acerca del autor

Hong Peng is the Professor at School of Computer and Software Engineering at Xihua University, Chengdu, China. The member of the “International Membrane Computing Society (IMCS)”, IEEE member and CCF member. He is the main investigator of 4 scientific research projects funded by National Natural Science Foundation of China and of more than 20 scientific research projects at the national and provincial levels. He was awarded Sichuan Provincial Natural Science Award in 2017. His research interests include membrane computing, artificial neural works, deep learning, image and computer vision, natural language processing. He has published over 170 scientific papers in international journals and conferences. He has more than 3200 citations with an H-index of 33, according to Google Scholar. 

Jun Wang is the Professor at School of Electrical Engineering and Electronic Information at Xihua University, Chengdu, China. The member of the “International Membrane Computing Society (IMCS)”. She is the main investigator of 3 scientific research projects funded by National Natural Science Foundation of China and of more than 30 scientific research projects at the national and provincial levels. She was awarded Sichuan Provincial Natural Science Award in 2017. Her research interests cover several topics, including membrane computing, artificial intelligence, machine learning, intelligent control, power systems. She has published over 90 scientific papers in international journals and conferences. He has more than 2300 citations with an H-index of 29, according to Google Scholar. 

De la contraportada

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.