An Evaluation of Confidence Bound Estimation Methods for Neural Networks

2002 ◽  
pp. 71-84 ◽  
Author(s):  
Luren Yang ◽  
Tom Kavli ◽  
Mats Carlin ◽  
Sigmund Clausen ◽  
Paul F. M. De Groot
Keyword(s):  
Neural Networks ◽  
Estimation Methods ◽  
Confidence Bound ◽  
Bound Estimation

scholarly journals Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7969
Author(s):  
Lianen Qu ◽  
Matthew N. Dailey
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Situation Awareness ◽  
Visual Motion ◽  
Accurate Method ◽  
Estimation Methods ◽  
Single Type ◽  
Global Localization ◽  
Trajectory Estimation

Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles.

CONSTRUCTIVE ESTIMATION OF APPROXIMATION FOR TRIGONOMETRIC NEURAL NETWORKS

2012 ◽  
Vol 10 (03) ◽  
pp. 1250021
Author(s):  
JIANJUN WANG ◽  
WEIHUA XU ◽  
BIN ZOU
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lower Bound ◽  
Upper Bound ◽  
Intrinsic Property ◽  
Order Of Approximation ◽  
Integrable Functions ◽  
Hidden Layer ◽  
Hidden Neurons ◽  
Bound Estimation

For the three-layer artificial neural networks with trigonometric weights coefficients, the upper bound and lower bound of approximating 2π-periodic pth-order Lebesgue integrable functions [Formula: see text] are obtained in this paper. Theorems we obtained provide explicit equational representations of these approximating networks, the specification for their numbers of hidden-layer units, the lower bound estimation of approximation, and the essential order of approximation. The obtained results not only characterize the intrinsic property of approximation of neural networks, but also uncover the implicit relationship between the precision (speed) and the number of hidden neurons of neural networks.

scholarly journals Classification of formant estimation methods in transformed auditory feedback experiments using convolutional neural networks

2020 ◽  
Vol 41 (5) ◽  
pp. 800-803
Author(s):  
Fumiaki Taguchi ◽  
Sadao Hiroya ◽  
Yasufumi Uezu ◽  
Takemi Mochida
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Auditory Feedback ◽  
Estimation Methods

scholarly journals Statistical post-processing of wind speed forecasts using convolutional neural networks

2021 ◽  
Author(s):  
Simon Veldkamp ◽  
Kirien Whan ◽  
Sjoerd Dirksen ◽  
Maurice Schmeits
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Convolutional Neural Networks ◽  
Weather Prediction ◽  
Estimation Methods ◽  
Post Processing ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Truncated Normal ◽  
Nwp Model

AbstractCurrent statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI’s deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best.

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

Counting the Hidden Defects in Software Documents

2010 ◽  
pp. 519-538
Author(s):  
Frank Padberg
Keyword(s):  
Neural Networks ◽  
Training Data ◽  
Estimation Methods ◽  
Model Parameters ◽  
Small Data ◽  
Data Sets ◽  
Software Inspection ◽  
Bayesian Techniques ◽  
Standard Quality ◽  
Small Data Sets

The author uses neural networks to estimate how many defects are hidden in a software document. Input for the models are metrics that get collected when effecting a standard quality assurance technique on the document, a software inspection. For inspections, the empirical data sets typically are small. The author identifies two key ingredients for a successful application of neural networks to small data sets: Adapting the size, complexity, and input dimension of the networks to the amount of information available for training; and using Bayesian techniques instead of cross-validation for determining model parameters and selecting the final model. For inspections, the machine learning approach is highly successful and outperforms the previously existing defect estimation methods in software engineering by a factor of 4 in accuracy on the standard benchmark. The author’s approach is well applicable in other contexts that are subject to small training data sets.

scholarly journals Neural Networks for self-adjusting Mutation Rate Estimation when the Recombination Rate is unknown

2021 ◽  
Author(s):  
Klara Elisabeth Burger ◽  
Peter Pfaffelhuber ◽  
Franz Baumdicker
Keyword(s):  
Neural Networks ◽  
Population Genetics ◽  
Mutation Rate ◽  
Estimation Method ◽  
Feedforward Neural Networks ◽  
Estimation Methods ◽  
Learning Tools ◽  
Effective Population ◽  
Recombination Rates ◽  
Optimal Estimators

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, the optimal linear estimation methods, namely Fu’s and Watterson’s estimator, are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more involved. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics.Here we investigate feedforward neural networks for the estimation of the mutation rate and compare their performance with the frequently used optimal estimators introduced by Fu and Watterson. We find that neural networks can reproduce the optimal estimators if provided with the appropriate features and training sets. Remarkably, only one hidden layer is necessary to obtain a single estimator that performs almost as well as the optimal estimators for both, low and high recombination rates and provides a superior estimation method for intermediate recombination rates at the same time.

scholarly journals Integration of Functional Link Neural Networks into a Parameter Estimation Methodology

2021 ◽  
Vol 11 (19) ◽  
pp. 9178
Author(s):  
Tuan-Ho Le ◽  
Mengyuan Tang ◽  
Jun Hyuk Jang ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Neural Networks ◽  
Robust Design ◽  
Least Squares Method ◽  
Estimation Method ◽  
Information Criterion ◽  
Primary Objective ◽  
Estimation Methods ◽  
Estimation Model ◽  
Functional Link ◽  
Dual Response

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.

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