ITERATIVE OPTIMIZATION METHOD OF GOB-VOLTERRA FILTERS

2005 ◽  
Vol 38 (1) ◽  
pp. 773-778 ◽  
Author(s):  
Alain Y. Kibangou ◽  
Gérard Favier ◽  
Moha M. Hassani
Keyword(s):  
Optimization Method ◽  
Volterra Filters ◽  
Iterative Optimization

scholarly journals Optimal Modelling of Steel Multi-Span Beams Using the Gradient-Iterative Method

2019 ◽  
Vol 29 (4) ◽  
pp. 112-127
Author(s):  
Leszek Mikulski
Keyword(s):  
Iterative Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Iterative Optimization ◽  
Basic Assumptions ◽  
The Web ◽  
Load Conditions

Abstract The article describes the gradient-iterative optimization method and outlines the method’s basic assumptions and illustrates its general use. The method’s implementation was illustrated based on a steel I-beam. The described calculation example concerns the optimization of the height of the web of a multi-span beam. The method enables finding an optimal solution with the use of simple and commonly available software. To illustrate the effectiveness of the optimization method, multiple calculations were performed for beams with various spans and various load conditions.

Non-linear Iterative Optimization Method for Locating Particles Using HPC Techniques

2014 ◽  
pp. 227-238
Author(s):  
Gloria Ortega ◽  
Julia Lobera ◽  
Inmaculada García ◽  
María del Pilar Arroyo ◽  
Gracia Ester Martín Garzón
Keyword(s):  
Optimization Method ◽  
Iterative Optimization ◽  
Non Linear

scholarly journals THE PROBABILISTIC NEURAL NET NEURON’S NUMBER CALCULATIONS

2014 ◽  
pp. 137-144
Author(s):  
Galina Shcherbacova ◽  
Victor Krylov ◽  
Oleg Logvinov
Keyword(s):  
Neural Network ◽  
Gradient Method ◽  
Probabilistic Neural Network ◽  
Optimization Method ◽  
Neural Net ◽  
Noise Stability ◽  
Iterative Optimization ◽  
Hidden Layer ◽  
Classification Time

The sub-gradient method of estimation of the number of the hidden layer neurons of a probabilistic neural network is suggested. This method allows evaluating the data compactness violation in λ-space. This evaluation based on the noise stability sub-gradient iterative optimization method. This method allows reducing the number of the hidden layer neurons and classification time.

Paper: An improved optimization method for finding a color filter to make a camera more colorimetric

2020 ◽  
Vol 2020 (15) ◽  
pp. 163-1-163-6 ◽  
Author(s):  
Graham D. Finlayson ◽  
Yuteng Zhu
Keyword(s):  
Imaging System ◽  
Optimization Method ◽  
Color Filter ◽  
Simple Extension ◽  
Spectral Transmittance ◽  
Iterative Optimization

Recently, an iterative optimization method was proposed that determines the spectral transmittance of a color filter which, when placed in front of a camera, makes the camera more colorimetric [1]. However, the performance of this method depends strongly on the filter (guess) that initializes the optimization. In this paper, we develop a simple extension to the optimization where we systematically sample the set of possible initial filters and for each initialization solve for the best refinement. Experiments demonstrate that improving the initialization step can result in the effective ‘camera+filter’ imaging system being much more colorimetric. Moreover, the filters we design are smoother than previously reported (which makes them easier to manufacture).

An iterative optimization method for estimating accelerometer bias based on gravitational apparent motion with excitation of swinging motion

2019 ◽  
Vol 90 (1) ◽  
pp. 015102 ◽  
Author(s):  
Tongwei Zhang ◽  
Yongjiang Huang ◽  
Haibing Li ◽  
Songbing Wang ◽  
Xiaole Guo ◽  
...  
Keyword(s):  
Apparent Motion ◽  
Optimization Method ◽  
Iterative Optimization
Sign in / Sign up

Export Citation Format

Share Document