Evolving Connectionist Systems
Download and Read Evolving Connectionist Systems full books in PDF, ePUB, and Kindle. Read online free Evolving Connectionist Systems ebook anywhere anytime directly on your device. We cannot guarantee that every ebooks is available!
Evolving Connectionist Systems
| Author | : Nikola K. Kasabov |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 465 |
| Release | : 2007-08-23 |
| Genre | : Computers |
| ISBN | : 1846283477 |
Download Evolving Connectionist Systems Book in PDF, Epub and Kindle
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Evolving Connectionist Systems Related Books
Language: en
Pages: 465
Pages: 465
Type: BOOK - Published: 2007-08-23 - Publisher: Springer Science & Business Media
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, ad
Language: en
Pages: 451
Pages: 451
Type: BOOK - Published: 2009-10-12 - Publisher: Springer
This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, ad
Language: en
Pages: 308
Pages: 308
Type: BOOK - Published: 2013-03-14 - Publisher: Springer Science & Business Media
Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioi
Language: en
Pages: 31
Pages: 31
Type: BOOK - Published: 1999 - Publisher:
Language: en
Pages: 742
Pages: 742
Type: BOOK - Published: 2018-08-29 - Publisher: Springer
Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monogra
