Identification Of Load Dynamics Using Artificial Neural Networks
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Moving Loads - Dynamic Analysis and Identification Techniques
The interaction phenomenon is very common between different components of a mechanical system. It is a natural phenomenon and is found with the impact force in aircraft landing; the estimation of degree of ripeness of an apple from impact on a beam; the interaction of the magnetic head of a computer disk leading to miniature development of modern c
Artificial Neural Networks - ICANN 2010
Author: Konstantinos Diamantaras
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-03
This three volume set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed proceedings of the 20th International Conference on Artificial Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 2010. The 102 revised full papers, 68 short papers and 29 posters presented were carefully reviewed and selected from 241 submissions. The first volume is divided in topical sections on ANN applications, Bayesian ANN, bio inspired – spiking ANN, biomedical ANN, computational neuroscience, feature selection/parameter identification and dimensionality reduction, filtering, genetic – evolutionary algorithms, and image – video and audio processing.
Identification of Load Dynamics Using Artificial Neural Networks
Author: Satish Gummadi
language: en
Publisher: LAP Lambert Academic Publishing
Release Date: 2012-02
It is expected that neural network can make the computer intelligent. The cognitive tasks easily done by the animals can be possible by the neural network computing. Neural network is a different kind of computing where learning and adaptation are possible in the computer program. There are different appoaches for intelligence and neural network is one such method. Intelligence can be in many ways. It may be computational, decision making, classification etc. The learing and knowledge storing capabilities are the superior qualities of the neural network algorithms. Neural networks find application in almost all the field of enginnering. The prominent applications of neural networks are fault classification, system modelling and identification, prediction and forecasting, speech recognition, image recognition etc.