Spiking Neural P Systems for Time Series Analysis
Language: English
Published by John Wiley and Sons Inc, US, 2025
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
£ 140.49
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Add to basketItem description from seller
An up-to-date and accurate discussion of spiking neural P systems in time series analysis In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis and examine the application models of time series analysis. You'll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction. The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding. Readers will also find: A thorough introduction to the theoretical and application research relevant to membrane computing and spiking P neural systemsComprehensive explorations of a variety of recurrent-like models for time series forecasting, including LSTM-SNP and GSNP modelsPractical discussions of common problems in reservoir computing models, including classification problemsComplete evaluations of models used in financial time series analysis, power load forecasting, and other techniques Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.…
Seller Inventory # LU-9781394381579
- Title
- Spiking Neural P Systems for Time Series Analysis
- Author
- Jun Wang, Hong Peng
- Publisher
- John Wiley and Sons Inc, US
- Publication year
- 2025
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1394381573
- ISBN 13
- 9781394381579
- Item weight
- 599 grams
An up-to-date and accurate discussion of spiking neural P systems in time series analysis
In Spiking Neural P Systems for Time Series Analysis, the authors explore the fundamentals and the current states of both spiking neural P systems and time series analysis and examine the application models of time series analysis. You’ll also find walkthroughs of recurrent-like, echo-like, and reservoir computing models for time series prediction.
The book covers applications in time series analysis such as financial time series analysis, power load forecasting, photovoltaic power forecasting, and medical signal processing, and contains illustrative photographs and tables designed to improve reader understanding.
Readers will also find:
- A thorough introduction to the theoretical and application research relevant to membrane computing and spiking P neural systems
- Comprehensive explorations of a variety of recurrent-like models for time series forecasting, including LSTM-SNP and GSNP models
- Practical discussions of common problems in reservoir computing models, including classification problems
- Complete evaluations of models used in financial time series analysis, power load forecasting, and other techniques
Perfect for scientists, researchers, postgraduates, lecturers, and teachers, Spiking Neural P Systems for Time Series Analysis will also benefit undergraduate students interested in advanced techniques for time series analysis.
"Synopsis" may belong to another edition of this title.
About the Author
Jun Wang, PhD, is a Professor in the School of Electrical Engineering and Electronic Information at Xihua University in Chengdu, China. Her research is focused on membrane computing, artificial intelligence, intelligence control. She has published over 90 scientific papers in international journals and conferences with an H-index of 35.
Hong Peng, PhD, is a Professor in the School of Computer and Software Engineering at Xihua University in Chengdu, China. His research interests include membrane computing, machine learning, pattern recognition. He has published over 230 scientific papers in international journals and conferences with an H-index of 42.
"About the title" may belong to another edition of this title.
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