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

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.

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Electrical Properties of Potassium Boro-Vanadate Glasses Doped with Iron

Materials Science Forum ◽

10.4028/www.scientific.net/msf.223-224.301 ◽

1996 ◽

Vol 223-224 ◽

pp. 301-306 ◽

Cited By ~ 1

Author(s):

H.R. Panchal ◽

D.K. Kanchan ◽

D.R.S. Somayajulu

Keyword(s):

Electrical Properties ◽

Vanadate Glasses

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A neutron diffraction investigation of the structure of phosphorus, barium and lead vanadate glasses

Journal of Non-Crystalline Solids ◽

10.1016/0022-3093(85)90009-2 ◽

1985 ◽

Vol 76 (2-3) ◽

pp. 333-350 ◽

Cited By ~ 37

Author(s):

Adrian C. Wright ◽

Colin A. Yarker ◽

Peter A.V. Johnson ◽

Roger N. Sinclair

Keyword(s):

Neutron Diffraction ◽

Lead Vanadate ◽

Vanadate Glasses

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Elastic properties and structural studies on lead–boro–vanadate glasses

Journal of Non-Crystalline Solids ◽

10.1016/j.jnoncrysol.2007.07.011 ◽

2008 ◽

Vol 354 (1) ◽

pp. 32-40 ◽

Cited By ~ 37

Author(s):

C. Narayana Reddy ◽

V.C. Veeranna Gowda ◽

R.P. Sreekanth Chakradhar

Keyword(s):

Elastic Properties ◽

Structural Studies ◽

Vanadate Glasses

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Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

13th Computers in Engineering Conference ◽

10.1115/cie1993-0053 ◽

1993 ◽

Author(s):

Shou-Heng Huang ◽

Ron M. Nelson

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Absolute Error ◽

Operating Conditions ◽

Delta Rule ◽

Rule Sets ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Set Up ◽

And Control

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.

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Generalized Delta Rule

Encyclopedia of Machine Learning and Data Mining ◽

10.1007/978-1-4899-7687-1_100181 ◽

2017 ◽

pp. 564-564

Keyword(s):

Delta Rule

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Learning reward frequency over reward probability: A tale of two learning rules

10.31234/osf.io/kcrqx ◽

2018 ◽

Author(s):

Hilary Don ◽

A Ross Otto ◽

Astin Cornwall ◽

Tyler Davis ◽

Darrell A. Worthy

Keyword(s):

Average Reward ◽

Binary Outcome ◽

Delta Rule ◽

Human Choice ◽

Presentation Frequency ◽

Learning Rules ◽

Expected Values ◽

Value Learning ◽

Average Rewards ◽

Delta Model

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.

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