9783843362580 - Optimisation and Modelling of Spiking Neural Networks: Enhancing Neural Information Processing Systems through the Power of Evolution di Schliebs, Stefan (6 risultati)

Taschenbuch. Condizione: Neu. Optimisation and Modelling of Spiking Neural Networks | Enhancing Neural Information Processing Systems through the Power of Evolution | Stefan Schliebs | Taschenbuch | 296 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843362580 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Motivated by the desire to better understand the truly remarkable information processing capabilities of the brain, numerous biologically plausible computational models have been explored in the recent decades. Already today, many applications employ neural networks to solve complex real world problems. Significant progress has been made in areas such as speech recognition, robotic controllers, associative memory and function approximation. This book develops an extension for a machine learning technique called the evolving spiking neural network (eSNN). It allows the automatic tuning of the neural and learning-related parameters of eSNN in order to promote its straightforward application to many different problem domains. The book proposes novel evolutionary algorithms capable of efficiently exploring multiple mixed-variable search spaces simultaneously. The enhanced eSNN is comprehensively investigated on benchmark problems and a real-world case study. 296 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Schliebs StefanReceived a MSc. in Computer Science at the University of Leipzig, Germany and a Ph.D. at the Auckland University of Technology, New Zealand. He is interested in novel neural information processing systems and their app.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Motivated by the desire to better understand the truly remarkable information processing capabilities of the brain, numerous biologically plausible computational models have been explored in the recent decades. Already today, many applications employ neural networks to solve complex real world problems. Significant progress has been made in areas such as speech recognition, robotic controllers, associative memory and function approximation. This book develops an extension for a machine learning technique called the evolving spiking neural network (eSNN). It allows the automatic tuning of the neural and learning-related parameters of eSNN in order to promote its straightforward application to many different problem domains. The book proposes novel evolutionary algorithms capable of efficiently exploring multiple mixed-variable search spaces simultaneously. The enhanced eSNN is comprehensively investigated on benchmark problems and a real-world case study.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 296 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Motivated by the desire to better understand the truly remarkable information processing capabilities of the brain, numerous biologically plausible computational models have been explored in the recent decades. Already today, many applications employ neural networks to solve complex real world problems. Significant progress has been made in areas such as speech recognition, robotic controllers, associative memory and function approximation. This book develops an extension for a machine learning technique called the evolving spiking neural network (eSNN). It allows the automatic tuning of the neural and learning-related parameters of eSNN in order to promote its straightforward application to many different problem domains. The book proposes novel evolutionary algorithms capable of efficiently exploring multiple mixed-variable search spaces simultaneously. The enhanced eSNN is comprehensively investigated on benchmark problems and a real-world case study.