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Kernel Least Mean Square Features for HMM-Based Signal Recognition

Seyed Hossein Ghafarian, Hadi Sadoghi Yazdi, Hamidreza Baradaran Kashani
2010 Journal of clean energy technologies  
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)  
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]

Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban, Suleyman Serdar Kozat
2020 arXiv   pre-print
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

Koushal Kumar, Gour Sundar Mitra Thakur
2012 International Journal of Information Technology and Computer Science  
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

Yong Yan, Lijuan Wang, Tao Wang, Xue Wang, Yonghui Hu, Quansheng Duan
2018 Flow Measurement and Instrumentation  
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

Olutobi Adeyemi, Ivan Grove, Sven Peets, Yuvraj Domun, Tomas Norton
2018 Sensors  
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]

Barbara Cunha
2022 arXiv   pre-print
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

Juying Dai, Jian Tang, Shuzhan Huang, Yangyang Wang
2019 Chinese Journal of Mechanical Engineering  
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]

Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
2020 arXiv   pre-print
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

Siniša Franjić, Dario Galić
2021 Advances in Computer and Communication  
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

N. Prasath, Vigneshwaran Pandi, Sindhuja Manickavasagam, Prabu Ramadoss
2021 Indonesian Journal of Electrical Engineering and Computer Science  
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)  
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

Khushboo Munir, Hassan Elahi, Afsheen Ayub, Fabrizio Frezza, Antonello Rizzi
2019 Cancers  
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

R. Nandhini Abirami, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, Chuan-Yu Chang, Dr Shahzad Sarfraz
2021 Complexity  
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]

Daulet Baimukashev, Bexultan Rakhim, Matteo Rubagotti, Huseyin Atakan Varol
2021 arXiv   pre-print
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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