A basic building block approach to CMOS design of analog neuro/fuzzy systems

Author(s):  
E. Vidal-Verdu ◽  
A. Vazquez-Vazquez ◽  
B. Linares-Barranco ◽  
E. Sanchez-Sinencio
1998 ◽  
pp. 357-390 ◽  
Author(s):  
Fernando Vidal-Verdú ◽  
Manuel Delgado-Restituto ◽  
Rafael Navas-González ◽  
Angel Rodríguez-Vázquez

Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto

2004 ◽  
Vol 338 (3) ◽  
pp. 611-629 ◽  
Author(s):  
Ashish V. Tendulkar ◽  
Anand A. Joshi ◽  
Milind A. Sohoni ◽  
Pramod P. Wangikar

2001 ◽  
Vol 676 ◽  
Author(s):  
Trent H. Galow ◽  
Andrew K. Boal ◽  
Vincent M. Rotello

ABSTRACTWe have developed a highly modular electrostatically-mediated approach to colloid-colloid and polymer-colloid networks using ‘building block’ and ‘bricks and mortar’ self-assembly methodologies, respectively. The former approach involved functionalization of one type of nanoparticle building block with a primary amine and a counterpart building block with a carboxylic acid derivative. After combining these two systems, acid-base chemistry followed by immediate charge-pairing resulted in the spontaneous formation of electrostatically-bound mixed-nanoparticle constructs. The shape and size of these ensembles were controlled via variation of particle size and stoichiometries. In the ‘bricks and mortar’ approach, a functionalized polymer is combined with complementary nanoparticles to provide mixed polymer-nanoparticle networked structures. A notable feature is the inherent porosity resulting from the electrostatic assembly. The shape and size of these ensembles were controlled via variation of particle size, stoichiometries and the order in which they were added.


2012 ◽  
Vol 05 (07) ◽  
pp. 477-482 ◽  
Author(s):  
Rafik Mahdaoui ◽  
Leila Hayet Mouss

2020 ◽  
Author(s):  
Ibraheem Kateeb ◽  
Larry Burton ◽  
Naser El-Bathy ◽  
Michael Peluso

Author(s):  
Julia Tholath Jose ◽  
Adhir Baran Chattopadhyay

Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.


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