The Classical Braess Paradox Problem Revisited: A Generalized Inverse Method on Non-Unique Path Flow Cases

2015 ◽  
Vol 16 (2) ◽  
pp. 605-622 ◽  
Author(s):  
Ming-Chorng Hwang ◽  
Hsun-Jung Cho
Keyword(s):  
Inverse Method ◽  
Generalized Inverse ◽  
Braess Paradox ◽  
Unique Path ◽  
Generalized Inverse Method

Heavy Vehicles Pass-by Noise Beamforming using Generalized Inverse Method and an Optimized Array

2013 ◽  
Author(s):  
Paulo Alexandre Galarce Zavala ◽  
José Roberto de França Arruda ◽  
Fábio Gimenes Bueno ◽  
Gaetano Miranda ◽  
Waldir Mothio ◽  
...  
Keyword(s):  
Inverse Method ◽  
Generalized Inverse ◽  
Heavy Vehicles ◽  
Generalized Inverse Method

Structural reanalysis for a modified structure

2008 ◽  
Vol 35 (9) ◽  
pp. 1018-1023 ◽  
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Inverse Method ◽  
Degrees Of Freedom ◽  
Generalized Inverse ◽  
Design Parameters ◽  
Initial Structure ◽  
Structural Elements ◽  
Static Behavior ◽  
Compatibility Conditions ◽  
Structural Reanalysis ◽  
Generalized Inverse Method

Structural reanalysis aims to determine the variations in the displacement of a structure due to the addition or deletion of elements without solving the full degrees of freedom. The iterations change the design parameters at each step and utilize the factorization of the stiffness matrix of the initial design. This study develops a new reanalysis method to determine the additional forces that act on the initial structure and the displacements of the modified structure. It utilizes the compatibility conditions at the interfaces between the initial structure and the added or deleted members as static constraints, and applies the generalized inverse method to describe the static behavior of the constrained structure. The structural elements that are added may be statically stable substructures or floating members that possess rigid-body freedom. Examples are included to show the effectiveness of the proposed method.

Optimization of controlled mechanism based on generalized inverse method

2006 ◽  
Vol 1 (3) ◽  
pp. 288-291
Author(s):  
Jin-tang Yang ◽  
Jian-yi Kong ◽  
He-gen Xiong ◽  
Guo-zhang Jiang ◽  
Gong-fa Li
Keyword(s):  
Inverse Method ◽  
Generalized Inverse ◽  
Generalized Inverse Method

scholarly journals Estimación de varianzas genéticas en maíz a partir de líneas S1 y S2

2016 ◽  
Vol 3 ◽  
pp. 9
Author(s):  
Félix V. Navarro ◽  
Wayne C. Youngquis ◽  
William Compton
Keyword(s):  
Genetic Variability ◽  
Inverse Method ◽  
Generalized Inverse ◽  
Selection Effect ◽  
Wide Sense ◽  
Family Selection ◽  
Additive Variance ◽  
Selection Cycle ◽  
The One ◽  
Generalized Inverse Method

The analysis of lines S-l and S-2 and the regression of the measurements of the S-2 on their corresponding S-l were used to estimate the existing genetic variability in a Nebraska Stiff Stalk Synthetic (NSS) corn population at two localities, Mead and Lincoln, Nebraska-USA. A significant genetic variability was found in NSS for grain yield, days to blooming, ear and plant height, grain humidity and lodging percentage. The S-2 lines showed more frequent interaction of genotypes x environment than their S-l. In the wide sense, the heritability for the yield calculated by the analysis of variance of S-2 lines was larger than the one based on the regression of the S-2 on S-l (60 and 42%, respectively). Eight models, originated from Cockerham (1983), were used to identify the existing types of genetic variabilities. The inverse matrix method was used to estimate the parameters of genetic variability when the used co-variances gave a non-singular square matrix. The generalized inverse method o Moore-Penrose was used when the models showed a rectangular matrix. Usually, the best model was the one which estimated the additive variance only. Often times, no consistent covariance estimates were obtained among additive and dominant homocygotic (D-1) effects. For it, we could not infer to what the S-l family selection effect could be on the behavior of the resulting line crosses. The expected genetic gain per selection cycle for yield of S-2 families was 11.4%.

scholarly journals Generalized inverse method for subspace maps

1983 ◽  
Vol 35 (4) ◽  
pp. 649-659 ◽  
Author(s):  
Saichi Izumino
Keyword(s):  
Inverse Method ◽  
Generalized Inverse ◽  
Generalized Inverse Method

scholarly journals A Lightweight Learning Method for Stochastic Configuration Networks Using Non-Inverse Solution

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 262
Author(s):  
Jing Nan ◽  
Zhonghua Jian ◽  
Chuanfeng Ning ◽  
Wei Dai
Keyword(s):  
Function Approximation ◽  
Inverse Method ◽  
Generalized Inverse ◽  
Low Complexity ◽  
High Complexity ◽  
Benchmark Function ◽  
Face Time ◽  
Generalized Inverse Method ◽  
Hidden Nodes ◽  
Real World Problems

Stochastic configuration networks (SCNs) face time-consuming issues when dealing with complex modeling tasks that usually require a mass of hidden nodes to build an enormous network. An important reason behind this issue is that SCNs always employ the Moore–Penrose generalized inverse method with high complexity to update the output weights in each increment. To tackle this problem, this paper proposes a lightweight SCNs, called L-SCNs. First, to avoid using the Moore–Penrose generalized inverse method, a positive definite equation is proposed to replace the over-determined equation, and the consistency of their solution is proved. Then, to reduce the complexity of calculating the output weight, a low complexity method based on Cholesky decomposition is proposed. The experimental results based on both the benchmark function approximation and real-world problems including regression and classification applications show that L-SCNs are sufficiently lightweight.

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