scholarly journals 'Tg-.DELTA. Rule' Applied to Semiconducting Vanadate Glasses Containing Different Amounts of Fe2O3.

1999 ◽  
Vol 107 (1245) ◽  
pp. 408-412 ◽  
Author(s):  
Tetsuaki NISHIDA ◽  
Jun IWASHITA ◽  
Shiro KUBUKI
Keyword(s):  
2001 ◽  
Vol 56 (6-7) ◽  
pp. 478-488
Author(s):  
Uwe Hoppe ◽  
Rainer Kranold ◽  
Emil Gattef ◽  
Jörg Neuefeind ◽  
David A. Keen

Abstract The short-range order of vitreous V20 5 and of three (Zn0)Jt(V20 5)1_x glasses with x = 0.2, 0.4, and 0.5 is studied by X-ray and neutron diffraction experiments where the change of the contrast allows to resolve the V -0 and Z n -0 correlations. The V -0 and the Z n -0 first-neighbor peaks are approximat­ ed by several Gaussian functions. In case of vitreous V20 5 two obvious V -0 distances exist which are related with V 0 4 and V 0 5 units. With ZnO additions the V -O coordination number decreases from 4.4 in vitreous V20 5 to 4.0 in the metavanadate glass where the strongest decrease of the fraction of V 0 5 units is found for glasses of * < 0.2. Dominantly, the V 0 5 groups are linked with the neighboring units by comers. The Z n-0 coordination numbers of the modified glasses are about five with closest dis­ tances of = 0.200 nm.


1996 ◽  
Vol 223-224 ◽  
pp. 301-306 ◽  
Author(s):  
H.R. Panchal ◽  
D.K. Kanchan ◽  
D.R.S. Somayajulu

1985 ◽  
Vol 76 (2-3) ◽  
pp. 333-350 ◽  
Author(s):  
Adrian C. Wright ◽  
Colin A. Yarker ◽  
Peter A.V. Johnson ◽  
Roger N. Sinclair

2008 ◽  
Vol 354 (1) ◽  
pp. 32-40 ◽  
Author(s):  
C. Narayana Reddy ◽  
V.C. Veeranna Gowda ◽  
R.P. Sreekanth Chakradhar

Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.


2018 ◽  
Author(s):  
Hilary Don ◽  
A Ross Otto ◽  
Astin Cornwall ◽  
Tyler Davis ◽  
Darrell A. Worthy

Learning about reward and expected values of choice alternatives is critical for adaptive behavior. Although human choice is affected by the presentation frequency of reward-related alternatives, this is overlooked by some dominant models of value learning. For instance, the delta rule learns average rewards, whereas the decay rule learns cumulative rewards for each option. In a binary-outcome choice task, participants selected between pairs of options that had reward probabilities of .65 (A) versus .35 (B) or .75 (C) versus .25 (D). Crucially, during training there were twice as many AB trials as CD trials, therefore option A was associated with higher cumulative reward, while option C gave higher average reward. Participants then decided between novel combinations of options (e.g., AC). Participants preferred option A, a result predicted by the Decay model, but not the Delta model. This suggests that expected values are based more on total reward than average reward.


2016 ◽  
Author(s):  
Sangeeta B. Kolavekar ◽  
N. H. Ayachit ◽  
Vinayak Pattar ◽  
R. V. Anavekar

1983 ◽  
Vol 57 (2) ◽  
pp. 305-325 ◽  
Author(s):  
C.F. Drake ◽  
B.W. James ◽  
H. Kheyrandish ◽  
B. Yates

Sign in / Sign up

Export Citation Format

Share Document