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Neural Network Modeling for Evaluating Sodium Temperature of Intermediate Heat Exchanger of Fast Breeder Reactor
2012
Advances in Computing
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The back propagation (BP) algorithm is used for training the network. ...
Further a model based on Radial Basis Function (RBF) neural network is developed and trained and the results are compared with standard back propagation algorithm. ...
Sri S.C Chetal, Director, IGCAR, Kalpakkam for his constant support and guidance for this project. ...
doi:10.5923/j.ac.20120202.03
fatcat:asgm25sj7nfehjks25swhsgcqi
Optimal Parameter Selection Using Three-term Back Propagation Algorithm for Data Classification
2017
International Journal on Advanced Science, Engineering and Information Technology
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The back propagation (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural Network. ...
Therefore, to resolve the inherent problems of BP algorithm, this paper proposed BPGD-A3T algorithm where the approach introduces three adaptive parameters which are gain, momentum and learning rate in ...
Yu and Liu [15] , proposed a back propagation algorithm with adaptive learning rate and momentum. ...
doi:10.18517/ijaseit.7.4-2.3387
fatcat:kg452yosarextdtka2l4kinpdy
An Improved Learning Algorithm Based On The Conjugate Gradient Method For Back Propagation Neural Networks
2008
Zenodo
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The conjugate gradient optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer ...
The proposed method improved the training efficiency of back propagation algorithm by adaptively modifying the initial search direction. ...
Second, the convergence rate of back-propagation is still too slow even if learning can be achieved. in the algorithm such as the learning rate and the momentum. ...
doi:10.5281/zenodo.1328444
fatcat:ha4kcihvjrfzvfhcafuogetxj4
Neural Network Based Numerical digits Recognization using NNT in Matlab
2013
International Journal of Computer Science & Engineering Survey
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The features of the number given by the user are extracted and compared with the feature database and the recognized number is displayed. ...
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of the important applications of artificial neural network is character Recognition. ...
ADAPTIVE LEARNING RATE The back propagation algorithms are basically of two types, gradient descent and gradient descent with momentum. ...
doi:10.5121/ijcses.2013.4502
fatcat:s75oacf55bgjbc6nryrbchse4i
Neural Networks With Random Letter Codes For Text-To-Phoneme Mapping And Small Training Dictionary
2006
Zenodo
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Also three types of encoding vectors for the input letters are analyzed and two training algorithms: the error back-propagation with momentum and fixed learning rate and the error back-propagation with ...
The training algorithm that used a fixed learning rate was the error back-propagation with momentum (see [1] , [2] and [10] for more details). ...
doi:10.5281/zenodo.53235
fatcat:rbowqltpandchccgqtxhzvz42e
Towards Food Security through Artificial Neural Network
2019
Journal of Science and Engineering
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The error generated is back propagated in order to adjust the weights of neural network. Images of the diseased leaves are identified with accuracy. ...
The disease identification is achieved through Image Processing technique and Back Propagation Neural Network. Features of images are extracted through binning pixels into eight Attribute Bins. ...
Adapting the learning rate requires some changes in the back propagation algorithm. ...
doi:10.3126/jsce.v6i0.23968
fatcat:lxugug4dzbdmzkw6b7me7w3j5q
A New Bat Based Back-Propagation (BAT-BP) Algorithm
[chapter]
2014
Advances in Intelligent Systems and Computing
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convergence rate. ...
The performance of the proposed Bat based Back-Propagation (Bat-BP) algorithm is compared with Artificial Bee Colony using BPNN algorithm (ABC-BP) and simple BPNN algorithm. ...
Artificial Bee Colony with Back-Propagation (ABC-BP) algorithm [16] [17] , and 3. ...
doi:10.1007/978-3-319-01857-7_38
fatcat:zdtoo3e2wjhqvlcrxs27fvctve
On Training Of Feed Forward Neural Networks
2007
Baghdad Science Journal
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In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation ...
algorithm has been used to increase the speed of training. ...
training.
1.Variable Learning Rate With standard gradient descent, the learning rate is held constant through out training. ...
doi:10.21123/bsj.4.1.158-164
fatcat:jd3pxgmkrveprnkcwakgijsrr4
Second Order Learning Algorithm for Back Propagation Neural Networks
2017
International Journal on Advanced Science, Engineering and Information Technology
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The simulation results clearly demonstrate that the proposed method significantly improves the convergence rate significantly faster the learning process of the general back propagation algorithm because ...
The new procedure computes and improves the search direction along the negative gradient by introducing the 'gain' value of the activation functions and calculating the negative gradient on an error with ...
(a) (b) Fig. 4 Output of the neural network training to learn a sine curve with and without using the adaptive gain in back propagation algorithm (a), and convergence speed for the sine function with ...
doi:10.18517/ijaseit.7.4.1956
fatcat:sy7qta7oufa37ld6cmpcohhvdu
Countering the Problem of Oscillations in Bat-BP Gradient Trajectory by Using Momentum
[chapter]
2013
Lecture Notes in Electrical Engineering
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Previously, a meta-heuristic search algorithm called Bat was proposed to train BPNN to achieve fast convergence in the neural network. ...
The performance of the modified Bat-BP algorithm is compared with simple Bat-BP algorithm on XOR and OR datasets. ...
learning rate. ...
doi:10.1007/978-981-4585-18-7_12
fatcat:nscw5krh7jesxgypcyfzziy7hq
Adaptive packet equalization for indoor radio channel using multilayer neural networks
1994
IEEE Transactions on Vehicular Technology
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In this paper, another fast packet-wise training algorithm with better convergence properties is derived on the basis of a recursive least-squares (RLS) routine. ...
To tackle this difficulty, a neural-based DFE is proposed to deal with the complex QAM signal over the complex-valued fading multipath radio channel without performing time-consuming complex-valued back-propagation ...
Since the gradient descent algorithm is globally convergent, this implies that the batch back-propagation algorithm is also globally convergent. ...
doi:10.1109/25.312768
fatcat:ocni6gyg5nbilmqyypnt2xtn3m
A microarray gene expression data classification using hybrid back propagation neural network
2014
Genetika
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This technical note applies hybrid models of Back Propagation Neural networks (BPN) and fast Genetic Algorithms (GA) to estimate the feature selection in gene expression data. ...
The back propagation method may execute the function of collaborate multiple parties. In existing method, collaborative learning is limited and it considers only two parties. ...
The hybrid algorithm of Back Propagation and Fast Genetic Algorithm will be designed to train and test the network. ...
doi:10.2298/gensr1403013v
fatcat:g2kxefiqqfedbp6of5ohhdr4wy
A cloning approach to classifier training
2002
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans
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It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm. ...
The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. ...
In Section IV, the current standard back-propagation algorithm (designated as BP), which includes momentum and an adaptive learning rate, is compared with the modified standard back-propagation algorithm ...
doi:10.1109/tsmca.2002.807035
fatcat:6xxgjniauncgngoetu5odqrpc4
Machine Learning with Resilient Propagation in Quaternionic Domain
2017
International Journal of Intelligent Engineering and Systems
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It achieves significantly faster learning over quaternionic domain back propagation (ℍ-BP) algorithm. ...
The slow convergence problem of back-propagation algorithm has been well combated by ℍ-RPROP. It has always demonstrated drastic reduction in the training cycles. ...
Inferences and discussions In this paper, we propose a fast and efficient learning algorithm ℍ-RPROP (resilient propagation in quaternionic domain); and its superiority over back-propagation algorithm ...
doi:10.22266/ijies2017.0831.22
fatcat:zfeqzk754fgq7dwpts5w6iqjya
Autism Spectrum Diagnosis using Adaptive Learning Algorithm for Multiple MLP Classifier
2021
Zenodo
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In addition, learning rate was constant on mot of the previous studies. In this paper, we used adaptive learning rate with the back propagation learning algorithm. ...
An adaptive learning rate algorithm is used to improve the convergence rate of the back-propagation as well. ...
doi:10.5281/zenodo.5188620
fatcat:rubklfngsndrhdbxxveyuvle7e
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