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Kernel Least Mean Square Features for HMM-Based Signal Recognition
2010
Journal of clean energy technologies
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Index Terms--Kernel least mean square, feature extraction, nonlinear prediction, linear predictive coding, signal recognition. ...
In this paper, an attempt is made to propose a new feature extraction method that is capable of capturing nonlinearities in signals. ...
In 2007, Stavrakoudis et al introduced Pipelined Recurrent Fuzzy neural networks (PRFNN). ...
doi:10.7763/ijcte.2010.v2.153
fatcat:xypjoz7vfra7vnmmsg2cyk7kcu
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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Recovery of Silent Data Corruption in Convolutional Neural Network Data Storage Mohammadreza Ramzanpour and Simone Ludwig .......... 3057 Auto-tuned Deep Recurrent Neural Networks for Application in Wind ...
(EEG) motorimagery classification with Long Short-TermMemory (LSTM) Neural Networks Charles Leon-Urbano and Willy Ugarte .......... 2814 A GA-Based Approach to Fine-Tuning BERT for Hate Speech Detection ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
[article]
2020
arXiv
pre-print
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We introduce a novel recurrent neural network (RNN) architecture, which adaptively switches between internal regimes in a Markovian way to model the nonstationary nature of the given data. ...
We investigate nonlinear regression for nonstationary sequential data. ...
Preliminaries We study nonlinear time series prediction with recurrent neural networks in nonstationary environments. ...
arXiv:2006.10119v1
fatcat:4calty7qrffnvpftwzlpe36bj4
Advanced Applications of Neural Networks and Artificial Intelligence: A Review
2012
International Journal of Information Technology and Computer Science
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It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. ...
The most general applications where neural networks are most widely used for problem solving are in pattern recognition, data analysis, control and clustering. ...
The MPEG movie sequences illustrate behaviors generated by dynamical recurrent neural network controllers co-evolved for pursuit and evasion capabilities is shown below in figure 2 . ...
doi:10.5815/ijitcs.2012.06.08
fatcat:pjdstjcnuzhwlg4rex2uwfrexe
Application of soft computing techniques to multiphase flow measurement: A review
2018
Flow Measurement and Instrumentation
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After extensive research and development over the past three decades, a range of techniques have been proposed and developed for online continuous measurement of multiphase flow. ...
This paper presents a comprehensive review of the soft computing techniques for multiphase flow metering with a particular focus on the measurement of individual phase flowrates and phase fractions. ...
A typical hybrid model is adaptive neuro-fuzzy inference system (ANFIS), in which a fuzzy inference system is implemented in the framework of adaptive networks. ...
doi:10.1016/j.flowmeasinst.2018.02.017
fatcat:6ohtbq5op5hdzik53mopvc3laa
Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling
2018
Sensors
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This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. ...
The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. ...
Acknowledgments: The authors wish to express their gratitude to the United Kingdom Centre for Ecology and Hydrology (CEH) for providing the dataset used for the study through the COSMOS UK project. ...
doi:10.3390/s18103408
pmid:30314346
pmcid:PMC6210977
fatcat:hrnag4lwjbf7lpvyw33l3ssvwi
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
[article]
2022
arXiv
pre-print
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The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. ...
Recurrent Neural Networks Recurrent Neural Networks (RNN) is a group of NN designed to handle dynamical systems, that is, systems changing over time. ...
Further comments are presented about Convolution Neural Networks and Recurrent Neural Network, for supervised learning of images and time series, respectively; Auto-encoders for unsupervised learning; ...
arXiv:2204.06362v1
fatcat:ayn6cpcn7nd65hum3z4fspxwrm
Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects
2019
Chinese Journal of Mechanical Engineering
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Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional ...
for fault recognition. ...
Acknowledgements The authors sincerely thanks to Professor Ting Rui of Army Engineering University for his critical discussion and reading during manuscript preparation. ...
doi:10.1186/s10033-019-0388-9
fatcat:lho5v4o7djhjbhfz2t33pd7as4
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
[article]
2020
arXiv
pre-print
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Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track ...
In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the ...
ACKNOWLEDGMENT Credit authors for icons made from www.flaticon.com. ...
arXiv:2001.11337v1
fatcat:cmurfjykjja3rdifr7e7cqq3wy
In Shortly about Neural Networks
2021
Advances in Computer and Communication
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Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. ...
The traditional term "neural network" refers to a biological neural network, i.e., a network of biological neurons. ...
Examples of such architectures include Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). ...
doi:10.26855/acc.2021.08.001
fatcat:7yucqdhy75b77jik3ues42ttue
A comparative and comprehensive study of prediction of Parkinson's disease
2021
Indonesian Journal of Electrical Engineering and Computer Science
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Conclusion and Future work: Most of the methods have used speech as a major attribute for their research and have produced substantial accuracy. ...
A range of those techniques, including SVM, Artificial Neural Network, Naive Bayes, Kernel based extreme learning through subtractive clustering landscapes, Random Forest, The Multi-Layer Perceptron with ...
Clustering and
Pattern Recognition
Speech
68.04
Wrapper Feature Selection
Scheme
72 to 92
Speech Signal Processing
Dysphonia
10
99
Aritificial Neural Network
Speech
85.92
Multilayer ...
doi:10.11591/ijeecs.v23.i3.pp1748-1760
fatcat:a2mlp3vn6ffcvjwubvilia47fe
Final Program
2019
2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
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He is a winner of Scopus prize for the best cited Mexican Scientists in Mathematics and Engineering 2010. ...
Sira-Ramírez is interested in the theoretical and practical aspects of feedback regulation of nonlinear dynamic systems, with special emphasis in: Algebraic methods, Active Disturbance Rejection, Variable ...
Algorithm Recurrent Neural Network Evaluation of a Recurrent Neural Network LSTM for the Detection of Exceedances of Particles PM10. ...
doi:10.1109/iceee.2019.8884545
fatcat:jm72uv7khvh3pab5bxvt7ow4xu
Cancer Diagnosis Using Deep Learning: A Bibliographic Review
2019
Cancers
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Boltzmann's machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN ...
In particular, deep neural networks can be successfully used for intelligent image analysis. ...
known as adaptive fuzzy inference neural network (AFINN). ...
doi:10.3390/cancers11091235
pmid:31450799
pmcid:PMC6770116
fatcat:ktuuttdu6zc7phj3mahp5yynxq
Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis
2021
Complexity
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The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. ...
Deep convolutional neural networks' applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. ...
network such as artificial neural network, convolutional neural network, recurrent neural network, or long short-term memory, whose task is to learn the data distribution. ...
doi:10.1155/2021/5541134
fatcat:xluxbl7kojbvxpjq5u726d3djm
End-to-End Deep Fault Tolerant Control
[article]
2021
arXiv
pre-print
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 accepted arXiv:2105.13598v3 fatcat:bruhvjtny5g6vmla7td4dmrnumOther Versions
The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time ...
As model-based FTC algorithms for nonlinear systems are often challenging to design, this paper focuses on a new method for FTC in the presence of sensor faults, based on deep learning. ...
Rather than focusing on a specific application, some
tolerant control, mechatronic systems, recurrent neural networks. ...
arXiv:2105.13598v1
fatcat:czvfzipyn5gmpoiuvtalf5igim
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