scholarly journals SYSTEM OF AUTOMATIC MICROSCOPIC ANALYSIS OF BIOMATERIALS FOR DIAGNOSTICS OF ONCOLOGICAL PATHOLOGIES USING TRAINED NEURAL NETWORKS AND TELEMEDICAL CONSULTATIONS

2019 ◽  
pp. 76-81
Author(s):  
Yu. S. Kucherov ◽  
V. N. Lobanov ◽  
V. S. Medovy ◽  
M. I. Cheldiev ◽  
P. B. Chuchkalov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Microscopic Analysis ◽  
Labor Intensity ◽  
Expert Evaluation ◽  
Population Surveys ◽  
Scanning Microscope ◽  
Neural Network Learning ◽  
Network Learning ◽  
Diagnostic Potential

Labor  intensity,  complexity  of  morphology,  the  shortage  of  qualified  specialists  do  not  allow  full  use  of  the  diagnostic  potential  of   microscopic  analysis  of  biomaterials  in  mass  population  surveys.  The  article  discusses  the  technology  of  creating  an  Automatic   Scan  Microscope  Analyzer  of  Oncological  Pathologies  (ASMAOP)  that  uses  neural  network  learning  during  regular  telemedicine   consultations  with  expert  evaluation  of  digital  copies  of  biomaterials  produced  by  a  scanning  microscope.  The  scheme  of  work  of   ASMAOP  in  the  composition  of  a  telemedicine  network,  hardware  solutions  including  platform  for  deep  learning  are  considered.   The  purpose  of  creation  of  ASMAOP  is  to  perform  microscopic  analyses  at  the  level  of  the  experienced  experts  with  a  significant   advantage  in  performance  and  availability.

Local Learning Algorithms for Sequential Tasks in Neural Networks

1998 ◽  
Vol 2 (6) ◽  
pp. 221-227 ◽  
Author(s):  
Anthony Robins ◽  
◽  
Marcus Frean ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithms ◽  
Learning Task ◽  
Sequential Learning ◽  
Local Learning ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sequential Task ◽  
Global And Local

In this paper, we explore the concept of sequential learning and the efficacy of global and local neural network learning algorithms on a sequential learning task. Pseudorehearsal, a method developed by Robins19) to solve the catastrophic forgetting problem which arises from the excessive plasticity of neural networks, is significantly more effective than other local learning algorithms for the sequential task. We further consider the concept of local learning and suggest that pseudorehearsal is so effective because it works directly at the level of the learned function, and not indirectly on the representation of the function within the network. We also briefly explore the effect of local learning on generalization within the task.

Adaptive Control Based on Neural Network for Ship Sterring Autopilot

2012 ◽  
Vol 503-504 ◽  
pp. 1239-1242
Author(s):  
Guan Shan Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reference Model ◽  
Learning Algorithm ◽  
Adaptive Controller ◽  
Membership Functions ◽  
Neural Network Learning ◽  
Network Learning ◽  
Ship Steering ◽  
Course Control

The Autopilot is importance for a ship to navigate safely and economically, so we proposes an intelligent reference modeling adaptive controller for ship steering based on neural networks. In order to satisfy the requirements of ship’s course control under various sea status, we used fuzzy logic and neural networks to design the feedback controller, used multilayer perceptron neural network to design the reference model and the identification network. In order to enhance adaptive characteristics of the controller，the parameters of membership functions and connection weights etc were revised online with neural network learning algorithm. The results of simulation shown that the performance of the ship controller is valuable and effective.

scholarly journals An Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism

Children ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 182
Author(s):  
Harshini Sewani ◽  
Rasha Kashef
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Data Exchange ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Unsupervised Neural Network

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and social interaction. Autism is a mental disorder investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning models to enhance clinicians’ ability to provide robust diagnosis and prognosis of autism. However, with dynamic changes in autism behaviour patterns, these models’ quality and accuracy have become a great challenge for clinical practitioners. We applied a deep neural network learning on a large brain image dataset obtained from ABIDE (autism brain imaging data exchange) to provide an efficient diagnosis of ASD, especially for children. Our deep learning model combines unsupervised neural network learning, an autoencoder, and supervised deep learning using convolutional neural networks. Our proposed algorithm outperforms individual-based classifiers measured by various validations and assessment measures. Experimental results indicate that the autoencoder combined with the convolution neural networks provides the best performance by achieving 84.05% accuracy and Area under the Curve (AUC) value of 0.78.

scholarly journals Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks

1996 ◽  
Vol 8 (4) ◽  
pp. 383-391
Author(s):  
Ju-Jang Lee ◽  
◽  
Sung-Woo Kim ◽  
Kang-Bark Park
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Rule ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Control Schemes ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Among various neural network learning control schemes, feedback error learning(FEL)8),9) has been known that it has advantages over other schemes. However, such advantages are founded on the assumption that the systems is linearly parameterized and stable. Thus, FEL has difficulties in coping with uncertain and unstable systems. Furthermore, it is not clear how the learning rule of FEL is obtained in the minimization sense. Therefore, to overcome such problems, we propose neural network control schemes using FEL with guaranteed performance. The proposed strategy is to use multi-layer neural networks, to design a stabilityguaranteeing controller(SGC), and to derive a learning rule to obtain the tracking performance. Using multilayer neural networks we can fully utilize the learning capability no matter how the system is linearly parameterized or not. The SGC makes it possible for the neural network to learn without fear of instability. As a result, the more the neural network learning proceeds, the better the tracking performance becomes.

scholarly journals Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

2018 ◽  
Vol 62 ◽  
pp. 69-100 ◽  
Author(s):  
Gustav Sourek ◽  
Vojtech Aschenbrenner ◽  
Filip Zelezny ◽  
Steven Schockaert ◽  
Ondrej Kuzelka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Expressive Power ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Neural Network Learning ◽  
Network Learning ◽  
First Order ◽  
Relational Structures ◽  
Efficient Learning

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.

scholarly journals Deep neural network learning of complex binary sorption equilibria from molecular simulation data

2019 ◽  
Vol 10 (16) ◽  
pp. 4377-4388 ◽  
Author(s):  
Yangzesheng Sun ◽  
Robert F. DeJaco ◽  
J. Ilja Siepmann
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Molecular Simulation ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Molecular Simulations ◽  
Simulation Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Sorption Equilibria

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling.

scholarly journals FINANCIAL FORECASTING USING NEURAL NETWORKS

2006 ◽  
Vol 1 ◽  
pp. 392
Author(s):  
A. Zorins
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Comparative Analysis ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Financial Forecasting ◽  
Neural Network Learning ◽  
Network Learning ◽  
Financial Time

This paper presents an application of neural networks to financial time-series forecasting. No additional indicators, but only the information contained in the sales time series was used to model and forecast stock exchange index. The forecasting is carried out by two different neural network learning algorithms – error backpropagation and Kohonen self-organising maps. The results are presented and their comparative analysis is performed in this article.

scholarly journals Application of a Neural Network for the Analysis of the “Soyuz” Launch Vehicle Boosters’ Separation.

2020 ◽  
pp. 42-56
Author(s):  
M.M. Matushin ◽  
D.A. Makhalov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Automated Analysis ◽  
General Information ◽  
Network Configuration ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Trained Neural Network

The paper discusses application of artificial intelligence (neural networks) technologies for automated analysis of dynamic processes of the “Soyuz” launch vehicle’s onboard systems. Cyclogram of strap-on boosters separa-tion as applied to this task, and telemetry measurement used to monitor this process are described. The general information about the construction of the used types of neural networks and about their learning using a back-propagation method is presented; the neural network configuration for solving the mentioned task, telemetry presentation format suitable for sup-plying power for the neural network, and features of the neural network learning are proposed. The approbation of the trained neural network for the analysis of launches of the “Soyuz-FG” and “Soyuz-2.1a” launch vehi-cles using telemetry in real-time and delayed modes was carried out.

scholarly journals Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5802
Author(s):  
Feng Zhang ◽  
Jiang Li ◽  
Ye Wang ◽  
Lihong Guo ◽  
Dongyan Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Parameter Optimization ◽  
Learning Algorithm ◽  
Neural Network Learning ◽  
Network Learning ◽  
Policy Optimization ◽  
Accuracy And Stability ◽  
Better Than

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

scholarly journals Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment

2021 ◽  
Author(s):  
Feng Zhang ◽  
Jiang Li ◽  
Ye Wang ◽  
Lihong Guo ◽  
Dongyan Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Parameter Optimization ◽  
Learning Algorithm ◽  
Neural Network Learning ◽  
Network Learning ◽  
Policy Optimization ◽  
Accuracy And Stability ◽  
Better Than

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of over-fitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) algorithm with the policy optimization and ensemble learning. This algorithm presents optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assessment the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

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