Journal ArticleDOI
Neural networks
Alberto Prieto,Beatriz Prieto,Eva M. Ortigosa,Eduardo Ros,Francisco J. Pelayo,Julio Ortega,Ignacio Rojas +6 more
TLDR
The development and evolution of different topics related to neural networks is described showing that the field has acquired maturity and consolidation, proven by its competitiveness in solving real-world problems.About:
This article is published in Neurocomputing.The article was published on 2016-11-19. It has received 184 citations till now. The article focuses on the topics: Neural modeling fields & Nervous system network models.read more
Citations
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Journal ArticleDOI
Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
TL;DR: The proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database and can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
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Recommendation system based on deep learning methods: a systematic review and new directions
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
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Deep learning approach for microarray cancer data classification
TL;DR: A deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes using a 7-layer deep neural network architecture having various parameters for each dataset is developed.
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Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
TL;DR: A thorough review on the development of ML-ELMs, including stacked ELM autoencoder, residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications, and the connection between random neural networks and conventional deep learning.
References
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Journal ArticleDOI
A Growing and Pruning Method for Radial Basis Function Networks
Maria Bortman,Mayer Aladjem +1 more
TL;DR: The modified GGAP training algorithm outperforms the original GGAP achieving both a lower prediction error and reduced complexity of the trained network.
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Artificial neural networks for non-stationary time series
TL;DR: This paper investigates whether it is feasible to relax the stationarity condition to non-stationary time series and finds that overfitting by ANN could be useful in the analysis of such non- stationary complex financial time series.
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Stability analysis of bidirectional associative memory networks with time delays
Chunhua Feng,Réjean Plamondon +1 more
TL;DR: By using the method of Liapunov functional, a model for bidirectional associative memory networks with time delays is studied and the asymptotic stability is global in the state space of the neuronal activations and is also independent of the delays.
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On the discrete-time dynamics of the basic Hebbian neural network node
TL;DR: The main contribution of this paper is the study of a deterministic discrete-time (DDT) formulation that characterizes the average evolution of the node, preserving the discrete- time form of the original network and gathering a more realistic behavior of the learning gain.
Journal ArticleDOI
Extracting rules from multilayer perceptrons in classification problems: a clustering-based approach
TL;DR: The proposed approach to extract rules from multilayer perceptrons trained in classification problems is experimentally evaluated in four datasets that are benchmarks for data mining applications and in a real-world meteorological dataset, leading to interesting results.