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Page 919 of The Journal of the Operational Research Society Vol. 51, Issue 8 [page]

2000 The Journal of the Operational Research Society  
Noise level Noisy data Data transformation (1) Moderate data transformation (2) Comprehensive data transformation Variable selection (2) Exhaustive variable selection Patients Net tolerance Network search  ...  The default value was 2. was used to determine when to stop training new networks. If no improvement occurred for ‘Net Tolerance’ iterations, training was halted and the best network was retained.  ... 

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior [article]

Yixing Huang, Alexander Preuhs, Guenter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier
2019 arXiv   pre-print
in noisy case compared with a state-of-the-art U-Net based method.  ...  methods are sensitive to noise.  ...  object while a U-Net based neural network with dense blocks [31] is utilized to predict the invisible ones.  ... 
arXiv:1908.06792v2 fatcat:ve63pxuy6ndx7fmqfp3b3efa2u

Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement [article]

Soumi Maiti, Michael I Mandel
2019 arXiv   pre-print
Here we propose to use the high quality speech generation capability of neural vocoders for better quality speech enhancement. We term this parametric resynthesis (PR).  ...  Next using these two vocoders and a new vocoder LPCNet, we evaluate the noise reduction quality of PR on unseen speakers and show that objective signal and overall quality is higher than the state-of-the-art  ...  Tolerance to error Finally, we measure the tolerance of PR models to inaccuracy of the prediction LSTM using the two best performing vocoders, WaveGlow and LPCNet.  ... 
arXiv:1911.06266v1 fatcat:nywlwlq4snfrlitoq744kl34z4

Empirical comparison of predictive models for mobile agents

A.E. Henninger, R. Madhavan
2004 Robotics and Autonomous Systems  
Specifically, this paper compares the performance of an extended Kalman filter based model, a neural network based model and a Newtonian based deadreckoning model, all used to predict an agent's trajectory  ...  Performance measures are presented over increasing levels of error tolerance.  ...  over a range of error tolerances.  ... 
doi:10.1016/j.robot.2004.07.023 fatcat:z7zn2q5alvgztgxwx25mstqen4

Page 74 of American Society of Civil Engineers. Collected Journals Vol. 11, Issue CP1 [page]

1997 American Society of Civil Engineers. Collected Journals  
The noise tolerance property of the self-organizing network is very useful under such circumstances.  ...  In this note, the network has been employed to predict the natural mode shapes of building frames with a varying number of stories.  ... 

An analysis of noise in recurrent neural networks: convergence and generalization

Kam-Chuen Jim, C.L. Giles, B.G. Horne
1996 IEEE Transactions on Neural Networks  
There has been much interest in applying noise to feedforward neural networks in order to observe their e ect on network performance.  ...  grammar using the predicted best noise model.  ...  ACKNOWLEDGEMENTS We would like to thank and acknowledge the reviewers for valuable comments and suggestions, and acknowledge Li Chun An for help in the simulations and useful discussions.  ... 
doi:10.1109/72.548170 pmid:18263536 fatcat:r5anvpkaqjfkfjjegmm67ivc4m

Road Traffic Noise Prediction with Neural Networks - A Review

Kranti KUMAR, Manoranjan PARIDA, Vinod Kumar KATIYAR
2012 An International Journal of Optimization and Control: Theories & Applications  
This paper aims to summarize the findings of research concerning the application of neural networks in traffic noise prediction.  ...  To overcome these problems, researchers and acoustical engineers have applied the artificial neural network in the field of traffic noise prediction.  ...  Acknowledgement The financial support in the form of grant in aid from the Council of Scientific and Industrial Research (CSIR), New Delhi, India, to one of the authors (Kranti Kumar) is gratefully acknowledged  ... 
doi:10.11121/ijocta.01.2012.0059 fatcat:t3taxfonorevxd5dmtqawabn2u

Comparison of T-Norms and S-Norms for Interval Type-2 Fuzzy Numbers in Weight Adjustment for Neural Networks

Fernando Gaxiola, Patricia Melin, Fevrier Valdez, Oscar Castillo, Juan Castro
2017 Information  
Noise of Gaussian type was applied to the testing data of the Mackey-Glass time series to demonstrate that the neural network using a interval type-2 fuzzy numbers method achieves a lower susceptibility  ...  To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey-Glass time series (for τ = 17).  ...  Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/info8030114 fatcat:jnsxz3tnr5cyfg2mogbup7d22e

Artificial neural networks in CT–PT contact detection in a PHWR

Abhijit Mukherjee, Pravin R Raijade, R.I.K Moorthy, A Kakodkar
1998 Nuclear Engineering and Design  
A counterpropagation neural network consisting of a Kohonen layer and a Grossberg layer has been employed. The noise tolerance of the network has been demonstrated.  ...  This paper demonstrates the power of artificial neural networks (ANNs) in diagnosing the CT -PT contact.  ...  Some ANN architectures have proved to be noise tolerant. This property can be very useful in the detection of contact.  ... 
doi:10.1016/s0029-5493(98)00173-3 fatcat:lqcu33j2dnfaldudiiymqmafwa

Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

Halil Ceylan, Kasthurirangan Gopalakrishnan, Mustafa Birkan Bayrak, Alper Guclu
2013 Nondestructive Testing and Evaluation  
The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in  ...  The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in  ...  "Noise-Tolerant Inverse Analysis Models for Non-Destructive Evaluation of Transportation Infrastructure Systems Using Neural Networks," Journal of Nondestructive Testing and Evaluation, Vol. 28, No. 3  ... 
doi:10.1080/10589759.2012.742084 fatcat:5s7dj575qvdn7o33wiih4nhoei

Noise-tolerant deep learning for histopathological image segmentation

Weizhi Li, Xiaoning Qian, Jim Ji
2017 2017 IEEE International Conference on Image Processing (ICIP)  
Two noise-tolerant deep learning architectures are proposed in this thesis. One is based on the Noisy at Random (NAR) Model, and the other is based on the Noisy Not at Random (NNAR) Model.  ...  The largest difference between the two is that NNAR based architecture assumes the label noise is dependent on features of the image.  ...  End to End Noise-Tolerant Network (NTN) 2 To alleviate the requirement of accurately segmented histo-images for u-net training, we propose to adjust the original u-net to be noise-tolerant following  ... 
doi:10.1109/icip.2017.8296848 dblp:conf/icip/LiQJ17 fatcat:2hrd4cajwbfxngvbtlo5pqjbe4

Page 2676 of Journal of Climate Vol. 12, Issue 8 [page]

1999 Journal of Climate  
Neu- ral net models were trained to various tolerance levels and tested for predictive skill since models trained to a high tolerance may be training on noise in the dataset.  ...  Experiments were done with neural net models with inputs corresponding to each of the stepwise multiple regression models.  ... 

A neuro-fuzzy tool for CT-PT contact detection in a pressurized heavy water reactor

A. Mukherjee, K. Shivaprasad, R.I.K. Moorthy, A. Kakodkar
2000 Engineering applications of artificial intelligence  
The performance of the network has been compared with that of the system-identi®cation techniques. The noise tolerance of the network is also demonstrated. 7  ...  The network consists of a cascade of self-organizing arti®cial neural networks (ANNs), along with fuzzy processors.  ...  To examine the noise-tolerance capability of the network, noise was introduced gradually from 210% to 230%.  ... 
doi:10.1016/s0952-1976(00)00024-5 fatcat:xgyt5oagcjbzxgaofl6xpaauaq

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Rytis Augustauskas, Arūnas Lipnickas
2020 Sensors  
Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and "Waterfall" connections, and attention gates to perform better  ...  The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead.  ...  (a) Label and (b) prediction of U-Net rendered on images from CrackForest and zoomed regions. Green, overlap of label and prediction; red, prediction pixels; yellow, prediction pixels.  ... 
doi:10.3390/s20092557 pmid:32365925 fatcat:atdcbyu4gngzjp34rno2px756i

Neural networks modelling and generalised predictive control for an autonomous underwater vehicle

Jianan Xu, Mingjun Zhang, Yujia Wang
2010 International journal of Modeling, identification and control  
This paper investigates the application of neural networks-based generalised predictive motion control for an autonomous underwater vehicle (AUV).  ...  The modified Elman neural networks (MENNs) are used as the multi-step predictive model, and the fused identification model is proposed to improve the predictive and control precision.  ...  Acknowledgements The authors would like to thank the PhD Programs Foundation of Ministry of Education of China (No: 20070217017) for their financial support.  ... 
doi:10.1504/ijmic.2010.035282 fatcat:xuyutehqerfnplz36vhk6lqjyu
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