نتایج جستجو برای: parameters tuning

تعداد نتایج: 621226  

Journal: :International Journal of Computer Applications 2012

2003
Kristiaan Pelckmans Jos De Brabanter Johan A. K. Suykens Bart De Moor

Model-free estimates of the noise variance are important for doing model selection and setting tuning parameters. In this paper a data representation is discussed which leads to such an estimator suitable for multidimensional input data. The visual representation called the differogram cloud is based on the 2-norm of the differences of the inputand output-data. A corrected way to estimate the v...

2014
Omar Bendjeghaba

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integralderivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted ...

Journal: :CoRR 2015
Nija Mani Gursaran Ashish Mani

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is...

Journal: :The Journal of neuroscience : the official journal of the Society for Neuroscience 2001
A Hanazawa H Komatsu

The texture of an object provides important cues for its recognition; however, little is known about the neural representation of texture. To investigate the representation of texture in the visual cortex, we recorded single-cell activities in area V4 of macaque monkeys. To distinguish the sensitivity of the cells to texture parameters such as density and element size from that to spatial frequ...

2016
Lajari Alandkar Sachin R. Gengaje Jun-Wei Hsieh Shih-Hao Yu Yung-Sheng Chen S. Kannan A. Sivasankar Thierry Bouwmans Fida El Baf Bertrand Vachon Lucia Maddalena Alfredo Petrosino Yannick Benezeth Pierre-Marc Jodoin Bruno Emile Helene Laurent Christophe Rosenberger

Object detection is one of the challenging steps in video surveillance. The most popular and robust technique for object detection is background subtraction. It is always challenging to obtain better performance of background subtraction algorithm as it requires appropriate initial tuning of common parameters like number of components in Gaussian Mixture Model (GMM), threshold, learning rate an...

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