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Adaptive Optimization
Algorithm Design
Artificial Neural Network
backpropagation algorithm
Chaotic Time Series
Conjugate Gradient
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(16)
ALEC: An Adaptive Learning Framework for Optimizing Artificial Neural Networks
Optimal Design of Neural Nets Using Hybrid Algorithms
The Evolution of Learning Algorithms for Artificial Neural Networks
Making use of population information in evolutionary artificial neural networks
Evolving artificial neural networks
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MetaLearning Evolutionary Artificial Neural Networks
MetaLearning Evolutionary Artificial Neural Networks,10.1016/S09252312(03)003692,Neurocomputing,Ajith Abraham
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MetaLearning Evolutionary Artificial Neural Networks
(
Citations: 67
)
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Ajith Abraham
In this paper, we present metalearning evolutionaryarti!cial
neural network
(MLEANN), an automatic computational framework for the
adaptive optimization
of arti!cial neural networks (ANNs) wherein the
neural network
architecture, activation function, connection weights;
learning algorithm
and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionallydesigned ANNs for
function approximation
problems. To evalu ate the comparative performance, we used three di5erent wellknown chaotic time series. We also present the stateoftheart popular
neural network
learning algorithms and some experimentation results related to convergence speed and generalization performance. We explored the perfor mance of
backpropagation
algorithm;
conjugate gradient
algorithm, quasiNewton algorithm and LevenbergMarquardt algorithm for the three chaotic time series. Performances of the di5erent learning algorithms were evaluated when the activation functions and architecture were changed. We further present the theoretical background, algorithm, design strategyand further demonstrate how e5ective and inevitable is the proposed MLEANN framework to design a neural network, which is smaller, faster and with a better generalization performance. c
Journal:
Neurocomputing  IJON
, vol. 56, pp. 138, 2004
DOI:
10.1016/S09252312(03)003692
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Citation Context
(42)
...To the best of our knowledge, only [
23
] approaches both parametric aspects and model configuration but in a manner specific to his context...
Tom'as Vidal Arredondo
,
et al.
Metalearning Based Optimization of Metabolic Pathway DataMining Infe...
...In particular, Evolving ANN (EANN) algorithms are becoming a popular solution, since they perform a global multipoint search, quickly locating areas of high quality, even when the search space is very complex [6,7,
8
,9]...
Juan Peralta Donate
,
et al.
Weighted CrossValidation Evolving Artificial Neural Networks to Forec...
...Neural network learning in general is a nonlinear minimization problem with many local minima [2] which depends on network weights, architecture (including number of hidden layers, number of hidden neurons and node transfer functions) and learning rules [
3
]...
Vahid Khorani
,
et al.
Artificial neural network weights optimization using ICA, GA, ICAGA a...
...Abraham [
14
] shows an automatic framework for optimization ANN in an adaptive way, and Xin Yao et. al. [15] try to spell out the future trends of the field...
Juan Peralta
,
et al.
Time series forecasting by evolving artificial neural networks using g...
...Nevertheless, Møller’s algorithm has been applied widely in solving largescale problems (
Abraham 2004;
Kashiyama et al. 2000; Mukkamala et al. 2005 ;S ozen et al. 2005; Tran et al. 2004)...
Bayram Cetisli
,
et al.
Speeding up the scaled conjugate gradient algorithm and its applicatio...
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Citations
(67)
Soft computing tool approach for texture classification using Discrete Cosine Transform
Pankaj H. Chandankhede
,
Parag V. Puranik
,
P. R. Bajaj
Conference:
International Conference on Electronic Computer Technology  ICECT
, 2011
Metalearning Based Optimization of Metabolic Pathway DataMining Inference System
Tom'as Vidal Arredondo
,
Wladimir O. Ormaz'abal
,
Diego Candel
,
Werner Creixell
Published in 2011.
Weighted CrossValidation Evolving Artificial Neural Networks to Forecast Time Series
Juan Peralta Donate
,
Paulo Cortez
,
Germ'an Guti'errez S'anchez
,
Araceli Sanchis de Miguel
Published in 2011.
Artificial neural network weights optimization using ICA, GA, ICAGA and RICAGA: Comparing performances
Vahid Khorani
,
Nafiseh Forouzideh
,
Ali Motie Nasrabadi
Conference:
IEEE Workshop on Hybrid Intelligent Models and Applications  HIMA
, 2011
Nonmonotone BFGStrained recurrent neural networks for temporal sequence processing
ChunCheng Peng
,
George D. Magoulas
Journal:
Applied Mathematics and Computation  AMC
, vol. 217, no. 12, pp. 54215441, 2011