A novel approach to noise-filtering based on a gain-scheduling neural network architecture

Cover of: A novel approach to noise-filtering based on a gain-scheduling neural network architecture |

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC], [Springfield, Va .

Written in English

Read online

Subjects:

  • Neural networks (Computer science)

Edition Notes

Book details

Other titlesNovel approach to noise filtering ....
StatementT. Troudet and W. Merrill.
SeriesNASA technical memorandum -- 106563.
ContributionsUnited States. National Aeronautics and Space Administration.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL14666700M

Download A novel approach to noise-filtering based on a gain-scheduling neural network architecture

A NOVEL APPROACH TO NOISE-FILTERING BASED ON A GAIN-SCHEDULING NEURAL NETWORK ARCHITECTURE T. Troudet Sverdrup Technology, Inc.

Lewis Research Center Group Brook Park, Ohio l National Aeronautics and Space Administration Lewis Research Center Cleveland, Ohio Abstract. A Gain-Scheduling Neural Network Architecture is proposed.

Get this from a library. A novel approach to noise-filtering based on a gain-scheduling neural network architecture. [Terry Troudet; United States. National Aeronautics and Space Administration.].

A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture. By W. Merrill and T.

Troudet. Abstract. A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness.

Author: W. Merrill and T. Troudet. A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture III T. Troudet, W. Merrill A Multiresolution Learning Method for Back-Propagation Networks III L Chan, W.

Chan A Neural Network Demodulator for Bandwidth Efficient. Seismic Noise Filtering Based on Generalized Regression Neural Networks Two Geoscience Applications by Optimal Neural Network Architecture.

Article. Dec The approach. In order to filter noise in datasets, Zeng and Martinez [9] presented neural network-based approach ANR. Each instance attaches a class probability vector to involve in neural network training. A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness.

In previous work [, we proposed a neural network based method to identify and correct mislabeled data. In this paper we present a noise filtering algorithm, called ANR (automatic noise reduction), to identify and remove mis- labeled instances in a data set.

ANR is based on the frame- work and mechanism of multi-layer neural networks trained by. This book covers real-world financial applications of neural networks, using the SOM approach, as well as introducing SOM methodology, software tools, and tips for processing.

illus. in color. A novel approach using artificial neural networks for representing chemical reactions is developed and successfully implemented with a modeled velocity-scalar joint pdf transport equation for H 2 /CO 2 turbulent jet diffusion flames. The chemical kinetics are represented using a three-step reduced mechanism, and the transport equation is solved by a Monte Carlo method.

3 Architecture of Adaptive Neural Network Models. A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application a Lagrange function.

Among clustering algorithms, because of the stable and flexible architecture of SOM neural networks, it has been used in a wide range of applications. Mangiameli et al.

[27] made a comparison between the self-organizing map neural network clustering and. We propose two novel models to improve word embeddings by unsupervised learning, in order to yield word denoising embeddings.

The word denoising embeddings are obtained by strengthening salient information and weakening noise in the original word embeddings, based on a deep feed-forward neural network. Li Y, Chung F and Wang S () A robust neuro-fuzzy network approach to impulse noise filtering for color images, Applied Soft Computing,(), Online publication date: 1-Mar Yu J, Xi L and Zhou X () Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA, Computers in.

A neural network adaptive filter is introduced for the removal of impulse noise in digital images. Using pixel classification by a self-organising neural network to detect the positions of the noisy pixels, the filter is able to effectively eliminate the impulses while retaining image integrity.

During the data collecting and labeling process it is possible for noise to be introduced into a data set. As a result, the quality of the data set degrades and experiments and inferences derived from the data set become less reliable.

In this paper we present an algorithm, called ANR (automatic noise reduction), as a filtering mechanism to identify and remove noisy data items whose classes. The data cleaning which is also known as noise filtering can be performed by using some algorithms based on ensembles.

An example homogenous ensemble filtering approach is illustrated in Fig. that is proposed in [19].Garcia-Gil et al. have implemented the filtering algorithm by using Apache Spark expressions that provide extending the MapReduce operation as shown below. A Noise Filtering Method Using Neural Networks Xinchuan Zeng and Tony Martinez Department of Computer Science Brigham Young University, Provo, UT, E-Mail: [email protected], [email protected] A= - During the data collecling and labeling process it is possible for noise to be introduced into a dato set.

As a. INTRODUCTION. Artificial neural networks (ANNs) are frequently used in computer-aided detection and diagnosis (CAD) applications. 1, 2 ANNs are popular because they are capable of modeling complicated classification decision boundaries from training data (of which the diagnostic truth status is known in every case) with minimal supervision or explicit modeling.

3, 4. This paper introduces a joint optimization method for the design of linear-phase FIR digital filters using the FRM technique. The method is based on a neural network approach (NNA) in which the coefficients of overall sub-filters are optimized simultaneously.

The NNA can be readily extended to multistage FRM filters design. The proposed approach is based on a simplified Third Generation Neural Network called Intersection Cortical Model (ICM).

Using the ICM output images, we can detect which pixel position corresponds to Salt and Pepper noise. Then, a selective Median filter is used for suppressing the Salt and Pepper noise only over the previously detected noisy. recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model.

The compact form of the network makes it possible to gener-ate 24 kHz bit audio 4 faster than real time on a GPU. Second, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. A Convolutional Fuzzy Neural Network Architecture for Object Classification with Small Training Database.

General Type-2 Fuzzy Gain Scheduling PID Controller with Application to Power-Line Inspection Robots. Novel Approach of Obtaining Dynamic Multi-attribute Weight for Intuitionistic Fuzzy Environment Based on Fractional Integrals.

Step 2. Choosing an Architecture. We use a convolutional Neural Network, to classify the spectrogram is because CNNs work better in detecting local feature patterns (edges etc) in different parts of the image and are also good at capturing hierarchical features which become subsequently complex with every layer as illustrated in the.

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application do-main and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that.

The network is able to remove the noise from the curves to a relatively high level but when I attempt to use some validation data on the network it states that I need to have input data of the same dimensions which makes me think it's considering all peaks to be one data set.

Optimal Artificial Neural Network Architecture Selection for Voting. Authors: Tim L. Andersen and Michael E. Rimer and Tony R. Martinez Abstract: This paper studies the performance of standard architecture selection strategies, such as cost/performance and CV based strategies, for voting methods such as bagging.

It is shown that standard architecture selection strategies are not optimal for. Jang, J.-S. and N. Gulley, “Gain scheduling based fuzzy controller design” Proc. of the International Joint Conference of the North American Fuzzy Information Processing Society Biannual Conference, the Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Join Technology Workshop on Neural Networks and Fuzzy Logic San Antonio, Texas Dec.

In this paper, a systematic approach for Neural Network (NN) controller design based on an incremental constructive layer algorithm is presented. The algorithm starts by considering minimal nodes in the hidden layer and choosing a pattern from those.

A Novel Approach to the Model Reference Adaptive Control of MIMO Systems. In: Proc. of the 19th International Workshop on Robotics in Alpe–Adria–Danube Region (RAAD ), Budapest, Hungary, Junepp.

31–36 () (CD issue, file: 4_raadpdf) Google Scholar. Step 2) Choosing an Architecture. We use a convolutional Neural Network, to classify the spectrogram is because CNNs work better in detecting local feature patterns (edges etc) in different parts of the image and are also good at capturing hierarchical features which become subsequently complex with every layer as illustrated in the.

Introduction. About one and a half decades ago, Barabási and Albert pointed out that the property of scale-invariance of many real networked systems originates from a specific growth process, named preferential attachment [].Since then, the study of complex networks has led to dramatic changes in many different fields [2–7], and also, many facets of node attractiveness in growing networks.

Chang B and Tsai H () Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity, Expert Systems with Applications: An International Journal,(), Online publication date: 1-Feb Adaptive filters have been used in wide range of signal processing applications because of its simplicity in computation and implementation.

Various adaptive algorithms available are associated with high computational complexity when implemented practically. This paper discusses the adaptive noise cancellation problem and how the neural network can be applied for weight convergence.

The same network structure can give rise to different fitness values due to different weight instantiations. Excess hidden layer neurons tend to fit the observed features of the training samples which are not representative of the intrinsic underlying distribution of observations, obstructing the characterization of the true properties of the system or problem.

Sanjay Talbar is a Professor in the Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, MS, India since He has also worked as a Professor and Head at Dr. Babasaheb Ambedkar Technological University, Lonere-Raigad from July May He has received B.E(Electronics Engineering) and M.

approach achieves state-of-the-art e ectiveness without us-ing sparse features, syntactic parsers, or external knowledge sources like WordNet. RELATED WORK There has been much recent work on applying neural net-works to answer selection [3, 4, 5, 22, 14, 13, 10]. Previ-ous work has been based on pointwise neural network mod-els.

A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (ERPs) in single trials.

Hu L(1), Mouraux A, Hu Y, Iannetti GD. Author information: (1)Department of Neuroscience, University College London, UK. M.-Y. Chow and J.

Teeter, “A Knowledge-Based Approach for Improved Neural Network Control of a Servomotor System with Nonlinear Friction Characteristics,” Mechatronics. There are several drawbacks to the EM-based approach described above.

The EM algorithm is a greedy optimization procedure that is notoriously known to get stuck in local optima. Another potential issue with combining neural networks and EM direction is scalability.

The framework requires training a neural network in each iteration of the EM. Perceptual learning specific to retinal position (1, 2), orientation (3–8), or scale (9–13) has been claimed to reflect plasticity in the adult early visual system (1, 2, 11, 12).But what is the nature of this plasticity?

The answer to this question stands at the interface between visual neuroscience and human performance.A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network’s learning ability and fuzzy if-then rule structure, is proposed in this paper.

The ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert.Wang D, Wan S and Guizani N () Context-based probability neural network classifiers realized by genetic optimization for medical decision making, Multimedia Tools and Applications,(), Online publication date: 1-Sep

13380 views Thursday, November 5, 2020