نتایج جستجو برای: back propagation algorithm

تعداد نتایج: 984050  

Journal: :middle east journal of cancer 0
shahram paydar trauma research center, shahid rajaee (emtiaz) trauma hospital, shiraz university of medical sciences, shiraz, iran saeedeh pourahmad department of biostatistics, shiraz university of medical sciences, shiraz, iran mohsen azad department of biostatistics, shiraz university of medical sciences, shiraz, iran shahram bolandparvaz trauma research center, shahid rajaee (emtiaz) trauma hospital, shiraz university of medical sciences, shiraz, iran reza taheri department of neurosurgery, shiraz university of medical sciences, shiraz, iran zahra ghahramani trauma research center, shahid rajaee (emtiaz) trauma hospital, shiraz university of medical sciences, shiraz, iran

background: clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the united states. in the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. a considerable fact is that sensitivity and specif...

2011
Frederik Eaton Justin Domke

Abstract This document will mainly be of interest to those who are re-implementing or extending “Choosing a Variable to Clamp” [1]. Most of the text is concerned with demonstrating a fairly straightforward isomorphism between forward and reverse-mode automatic differentiation applied to the belief propagation algorithm, in section 3. This shows that “Back-Belief Propagation” of [1] is performin...

2009
Nazri Mohd Nawi R. S. Ransing Mohd Najib Mohd Salleh Rozaida Ghazali Norhamreeza Abdul Hamid Tun Hussein

We proposed a method for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. In a ‘feed forward’ algorithm, the slope of the activation function is directly influenced by a parameter referred to as ‘gain’. In this paper, the influence of the adaptive gain on the learning ability of a neural network is analysed. Multi layer fee...

2007
ZhiQiang Zhang Zheng Tang Catherine Vairappan

− Elman Neural Network (ENN) have been efficient identification tool in many areas since they have dynamic memories. However, the local minima problem usually occurs in the process of the learning because of the employed back propagation algorithm. In this paper, we propose a novel learning method for ENN by introducing adaptive learning parameter into the traditional local search algorithm. Th...

2013
Ali Kattan Rosni Abdullah

The Harmony Search algorithm is relatively a young stochastic meta-heuristic that was inspired from the improvisation process of musicians. HS has been successfully applied as an optimization method in many scientific and engineering fields and was reported to be competitive alternative to many rivals. In this work a new framework is presented for adapting the HS algorithm as a method for the s...

2012
Puspanjali Mohapatra Alok Raj

This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices for one day, one week, two weeks and one month in advance. The Indian stock prices i.e. BSE (Bomba...

2013
Nazri Mohd Nawi Abdullah Khan Mohammad Zubair Rehman

Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper pr...

2015
M. Padmavathi T. V. N. Sudheer

In comparison with hard clustering methods, in which a pattern belongs to a unique cluster, clustering algorithms with fuzziness allow patterns with differing degrees of membership to belong to all clusters. This is important in domains such as sentence clustering, as a sentence may belong to more than a topic present within a document or set of documents. Since most sentence similarity measure...

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