Relevant bibliographies by topics / Ensemble neural noise

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Ensemble neural noise'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Ensemble neural noise"

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Timme, Nicholas M., David Linsenbardt, and Christopher C. Lapish. "A Method to Present and Analyze Ensembles of Information Sources." Entropy 22, no. 5 (May 21, 2020): 580. http://dx.doi.org/10.3390/e22050580.

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Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a pure
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Nanni, Loris, Gianluca Maguolo, Sheryl Brahnam, and Michelangelo Paci. "An Ensemble of Convolutional Neural Networks for Audio Classification." Applied Sciences 11, no. 13 (June 22, 2021): 5796. http://dx.doi.org/10.3390/app11135796.

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Research in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of endangered species in different habitats or the type of interfering noise in urban environments. Since environmental audio datasets are often limited in size, a robust model able to perform well across different datasets is of strong research interest. In this paper, ensembles of classifiers are combined that exploit six data augmentation techniques and four signal representation
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Sheng, Chunyang, Haixia Wang, Xiao Lu, Zhiguo Zhang, Wei Cui, and Yuxia Li. "Distributed Gaussian Granular Neural Networks Ensemble for Prediction Intervals Construction." Complexity 2019 (July 3, 2019): 1–17. http://dx.doi.org/10.1155/2019/2379584.

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To overcome the weakness of generic neural networks (NNs) ensemble for prediction intervals (PIs) construction, a novel Map-Reduce framework-based distributed NN ensemble consisting of several local Gaussian granular NN (GGNNs) is proposed in this study. Each local network is weighted according to its contribution to the ensemble model. The weighted coefficient is estimated by evaluating the performance of the constructed PIs from each local network. A new evaluation principle is reported with the consideration of the predicting indices. To estimate the modelling uncertainty and the data noise
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Chaouachi, Aymen, Rashad M. Kamel, and Ken Nagasaka. "Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 69–75. http://dx.doi.org/10.20965/jaciii.2010.p0069.

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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results
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Noh, Kyoungjin, and Joon-Hyuk Chang. "Deep neural network ensemble for reducing artificial noise in bandwidth extension." Digital Signal Processing 102 (July 2020): 102760. http://dx.doi.org/10.1016/j.dsp.2020.102760.

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He, Lei, Xiaohong Shen, Muhang Zhang, and Haiyan Wang. "Discriminative Ensemble Loss for Deep Neural Network on Classification of Ship-Radiated Noise." IEEE Signal Processing Letters 28 (2021): 449–53. http://dx.doi.org/10.1109/lsp.2021.3057539.

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Dai, Feng Yan, Zhao Yao Shi, and Jia Chun Lin. "Research of Defect Detection Method Noise for Bevel Gear." Advanced Materials Research 889-890 (February 2014): 722–25. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.722.

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Noise signal analysis method is widely available for gearbox bevel gear fault detection. However, the noise from the gearbox is usually concealed by background noise, which leads to poor efficiency analysis. This paper reports an ensemble empirical mode decomposition (EEMD) and neural network method for bevel gear fault detection. To extract useful signal, EEMD algorithm was firstly applied to get rid of the background noise. Characteristics from a group of discriminating defect status were then chosen to build the eigenvector. Finally, the eigenvector was imported into a back propagation (BP)
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Et. al., Rajesh Birok,. "ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2382–89. http://dx.doi.org/10.17762/turcomat.v12i2.2033.

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Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morpholo
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Jin, Dequan, Jigen Peng, and Bin Li. "A New Clustering Approach on the Basis of Dynamical Neural Field." Neural Computation 23, no. 8 (August 2011): 2032–57. http://dx.doi.org/10.1162/neco_a_00153.

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In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.
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Chen, Kai, Kai Xie, Chang Wen, and Xin-Gong Tang. "Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition." Sensors 20, no. 12 (June 15, 2020): 3373. http://dx.doi.org/10.3390/s20123373.

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is imp
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Dissertations / Theses on the topic "Ensemble neural noise"

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Brown, Daniel. "Origins and use of the stochastic and sound-evoked extracellular activity of the auditory nerve." University of Western Australia. Dept. of Physiology, 2007. http://theses.library.uwa.edu.au/adt-WU2008.0082.

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[Truncated abstract] The present study investigated whether any of the characteristics of the compound action potential (CAP) waveform or the spectrum of the neural noise (SNN) recorded from the cochlea, could be used to examine abnormal spike generation in the type I primary afferent neurones, possibly due to pathologies leading to abnormal hearing such as tinnitus or tone decay. It was initially hypothesised that the CAP waveform and SNN contained components produced by the local action currents generated at the peripheral ends of the type I primary afferent neurones, and that changes in the
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Gómez, Cerdà Vicenç. "Algorithms and complex phenomena in networks: Neural ensembles, statistical, interference and online communities." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7548.

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Aquesta tesi tracta d'algoritmes i fenòmens complexos en xarxes.<br/><br/>En la primera part s'estudia un model de neurones estocàstiques inter-comunicades mitjançant potencials d'acció. Proposem una tècnica de modelització a escala mesoscòpica i estudiem una transició de fase en un acoblament crític entre les neurones. Derivem una regla de plasticitat sinàptica local que fa que la xarxa s'auto-organitzi en el punt crític.<br/><br/>Seguidament tractem el problema d'inferència aproximada en xarxes probabilístiques mitjançant un algorisme que corregeix la solució obtinguda via belief propagation
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Dam, Hai Huong Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "A scalable evolutionary learning classifier system for knowledge discovery in stream data mining." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38865.

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Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the
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Bharmauria, Vishal. "Investigating the encoding of visual stimuli by forming neural circuits in the cat primary visual cortex." Thèse, 2016. http://hdl.handle.net/1866/14129.

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Contexte La connectomique, ou la cartographie des connexions neuronales, est un champ de recherche des neurosciences évoluant rapidement, promettant des avancées majeures en ce qui concerne la compréhension du fonctionnement cérébral. La formation de circuits neuronaux en réponse à des stimuli environnementaux est une propriété émergente du cerveau. Cependant, la connaissance que nous avons de la nature précise de ces réseaux est encore limitée. Au niveau du cortex visuel, qui est l’aire cérébrale la plus étudiée, la manière dont les informations se transmettent de neurone en neurone
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Book chapters on the topic "Ensemble neural noise"

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Çatak, Ferhat Özgür. "Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM." In Neural Information Processing, 10–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26535-3_2.

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Libralon, Giampaolo L., André C. Ponce Leon Ferreira Carvalho, and Ana C. Lorena. "Ensembles of Pre-processing Techniques for Noise Detection in Gene Expression Data." In Advances in Neuro-Information Processing, 486–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_60.

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Szukalski, Szymon K., Robert J. Cox, and Patricia S. Crowther. "Using Artificial Neural Network Ensembles to Extract Data Content from Noisy Data." In Lecture Notes in Computer Science, 974–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_137.

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Festag, Sven, and Cord Spreckelsen. "Semantic Anomaly Detection in Medical Time Series." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210059.

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The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based on recurrent neural networks with gated recurrent units were used for the semantic encoding of such time frames. A subsequent cluster analysis conducted in the code space served as the decision mechanism labelling samples as anomalies or normal intervals, respectively. The cluster ensemble method called cluster-based similarity partitioning proved itself well suited for this task when used in combination with density-based spatial clustering of applications with noise. The best performing system reached an adjusted Rand index of 0.11 on real-world ECG signals labelled by medical experts. This corresponds to a precision and recall regarding the detection task of around 0.72. The new general approach outperformed several state-of-the-art outlier recognition methods and can be applied to all kinds of (medical) time series data. It can serve as a basis for more specific detectors that work in an unsupervised fashion or that are partially guided by medical experts.
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Conference papers on the topic "Ensemble neural noise"

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Zhihua, Gao, Ben Kerong, and Cui Lilin. "Noise Source Recognition Based on Two-Level Architecture Neural Network Ensemble for Incremental Learning." In 2009 International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 2009. http://dx.doi.org/10.1109/dasc.2009.11.

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Ak, Ronay, Moneer M. Helu, and Sudarsan Rachuri. "Ensemble Neural Network Model for Predicting the Energy Consumption of a Milling Machine." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47957.

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Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of existing prediction models aim at providing only point predictions and can be affected by uncertainties in the model parameters and input data. In this paper, a prediction model that generates prediction intervals (PIs) for estimating energy consumption of a milling machine is proposed. PIs are used to provide information on the confidence in the prediction by accounting for the uncertainty in both the model parameters and the noise in the input variables. An ensemble mod
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Sengupta, Ushnish, Carl E. Rasmussen, and Matthew P. Juniper. "Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14904.

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Abstract Experiments are performed on a turbulent swirling flame placed inside a vertical tube whose fundamental acoustic mode becomes unstable at higher powers and equivalence ratios. The power, equivalence ratio, fuel composition and boundary condition of this tube are varied and, at each operating point, the combustion noise is recorded. In addition, short acoustic pulses at the fundamental frequency are supplied to the tube with a loudspeaker and the decay rates of subsequent acoustic oscillations are measured. This quantifies the linear stability of the system at every operating point. Us
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Wu, Tsung-Liang, and Yu-Chun Hwang. "Failure Detection for Multiple Micro-Punches Outfitted in Progressive Piercing Processes With Artificial Intelligent Model." In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7494.

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Abstract The purpose of this study is to establish a model for the diagnosis of multiple micro-punch failures. The punch is assumed to a rigid body structure with a small change in the stiffness during the piercing process and its diameter is varied between Ø0.8–1.2 mm. Thus, the wearing trend of multiple punches in the piercing process and source of the interfered signals make it extremely difficult to analyze. The two major challenges that affect punch failure estimation are the poor signal-to-noise ratio within the factory environment and the rigid body mode disturbance in the signal. To ac
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Yang, Dongdong, Senzhang Wang, and Zhoujun Li. "Ensemble Neural Relation Extraction with Adaptive Boosting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/630.

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Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equall
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Hartono, P., and S. Hashimoto. "Effective learning in noisy environment using neural network ensemble." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.857894.

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Yang, Lijian, Ya Jia, and Ming Yi. "The effects of electrical coupling on the temporal coding of neural signal in noisy Hodgkin-Huxley neuron ensemble." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5583237.

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He, Kexin, Yuhan Shen, and Wei-Qiang Zhang. "Multiple Neural Networks with Ensemble Method for Audio Tagging with Noisy Labels and Minimal Supervision." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/r7nr-v396.

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