Results 151 to 160 of about 20,311 (188)

Data imputation via evolutionary computation, clustering and a neural network

Neurocomputing, 2015
In this paper, two novel hybrid imputation methods involving particle swarm optimization (PSO), evolving clustering method (ECM) and autoassociative extreme learning machine (AAELM) in tandem are proposed, which also preserve the covariance structure of the data.
Chandan Gautam, Vadlamani Ravi
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On generalisation of machine learning with neural-evolutionary computations

Proceedings Third International Conference on Computational Intelligence and Multimedia Applications. ICCIMA'99 (Cat. No.PR00300), 2003
Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such as network size, initial conditions, learning rate, weight decay factor, pruning algorithms, and many more.
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Evolving Computational Neural Networks Through Evolutionary Computation

2001
Computational neural networks [CNNs] have been used widely in many application areas in recent years. Most applications use feedforward CNNs and the backpropagation [BP] training algorithm. There are numerous variants of the classical BP algorithm and other training algorithms, but all these training algorithms assume a fixed CNN architecture.
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Automating Stock Prediction with Neural Network and Evolutionary Computation

2003
In the previous studies [1, 2, 3], it has been found that there is strong correlation between the US market and the Asian markets in the long run. The VAR analysis shows that the US indices lead the Asian ones. But, such correlation is time-dependent and affects the performance of using the historical US data to predict the Asian markets by neural ...
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Neural networks and evolutionary computation. Part II: hybrid approaches in the neurosciences

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 2002
For pt. I, see ibid., p. 268. This paper focuses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences. It provides an overview of hybrid work done in the neurosciences, and surveys neuroscientific theories that are bridging the gap between neural ...
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Evolving Artificial Neural Networks through L-system and evolutionary computation

2015 International Joint Conference on Neural Networks (IJCNN), 2015
The aim of this paper is to present a biologically inspired Neuro Evolutive Algorithm (NEA) able to generate modular, hierarchical and recurrent neural structures as those often found in the nervous system of live beings, and that enable them to solve intricate survival problems. In our approach we consider that a nervous system design and organization
Lídio Mauro Lima de Campos, Roberto Célio Limão de Oliveira, Mauro Roisenberg   +2 more
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Evolutionary Computing and Type-2 Fuzzy Neural Networks

2014
This chapter deals with comparative analysis of methods of evolutionary computing and its merging with type-2 fuzzy neural networks. The models of neural networks and, especially, fuzzy and high-order fuzzy networks can be described by complex nonlinear, non-convex, and non-differentiable functions.
Rafik Aziz Aliev, Babek Ghalib Guirimov
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Adaptation and Learning for Pattern Recognition: A Comparison Between Neural and Evolutionary Computation

2001
The work presented in this chapter is aimed at developing self-governing artificial systems that are able to operate in complex, uncertain and dynamic application domains, by mimicking the learning and adaptation capabilities exhibited by biological systems.
C. DE STEFANO, DELLA CIOPPA, Antonio, MARCELLI, Angelo   +2 more
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Evolutionary training of deep neural networks on heterogeneous computing environments

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
Subodh Kalia, Chilukuri K. Mohan, Ramakrishna R. Nemani   +2 more
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Neural networks and evolutionary computation. I. Hybrid approaches in artificial intelligence

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 2002
This paper focuses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences. It provides a comprehensive and compact overview of hybrid work done in artificial intelligence, and shows the state of the art of combining artificial neural networks and ...
openaire   +1 more source