Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters: Utilizing High-Dimensional Parameters

Ön Kapak
Nitta, Tohru
Idea Group Inc (IGI), 28 Şub 2009 - 504 sayfa

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications.

Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent years. Graduate students and researchers will easily acquire the fundamental knowledge needed to be at the forefront of research, while practitioners will readily absorb the materials required for the applications.

 

Seçilmiş sayfalar

İçindekiler

Applications of ComplexValued Neural Networks
142
Models with HighDimensional Parameters
324
Compilation of References
440
About the Contributors
470
Index
476
Telif Hakkı

Diğer baskılar - Tümünü görüntüle

Complex-valued Neural Networks: Utilizing High-dimensional Parameters
Tohru Nitta
Önizleme Yok - 2009

Sık kullanılan terimler ve kelime öbekleri

activation function adaptive affine transformation algorithm analysis applications approach approximation associative memory blind Boltzmann calculated channel chapter communication complex complex-valued neural networks component computation connection consider continuous convergence defined denotes derived described distribution dynamics Electronics energy function engineering equalization equation error estimation example existence Figure given global hidden Hopfield IEEE Trans imaginary initial input International introduced layer learning linear Machine matrix method mode neuron nonlinear obtained operation optimization output parameters patterns performance phase Physics presented probability problem Processing properties proposed quantum mechanics quaternionic Qubit real-valued recurrent relation respectively rule satisfy Science separation shown shows signal Signal Processing simulation solution solve space stability stochastic stored structures Theorem theory tion Transactions transform University update values vector weights

Yazar hakkında (2009)

Tohru Nitta received a BS degree in mathematics, MS and PhD degrees in information science from University of Tsukuba (Japan) in 1983, 1985, and 1995 respectively. From 1985 to 1990, he was with NEC Corporation and engaged in research on expert systems. He joined the Electrotechnical Laboratory, Agency of Industrial Science and Technology, Ministry of International Trade and Industry (1990). He is currently a senior research Scientist in National Institute of Advanced Industrial Science and Technology (former Electrotechnical Laboratory), Japan. He was also with Department of Mathematics, Graduate School of Science, Osaka University as an associate professor from 2000 to 2006, and as a professor from 2006 to 2008 (additional post). His research interests include complex adaptive systems such as neural networks. [Editor]

Kaynakça bilgileri

BaşlıkComplex-Valued Neural Networks: Utilizing High-Dimensional Parameters: Utilizing High-Dimensional Parameters
Premier reference source
EditörNitta, Tohru
Baskıresimli
YayıncıIdea Group Inc (IGI), 2009
ISBN1605662151, 9781605662152
Uzunluk504 sayfa
  
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