scholarly journals The neural network pushdown automaton: Architecture, dynamics and training

1998 ◽  
pp. 296-345 ◽  
Author(s):  
G. Z. Sun ◽  
C. L. Giles ◽  
H. H. Chen
Keyword(s):  
Neural Network ◽  
Pushdown Automaton ◽  
The Neural Network ◽  
And Training

scholarly journals APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN THE DENTAL CARIES FORECAST

2020 ◽  
Vol 73 (7) ◽  
pp. 1499-1504
Author(s):  
Oleksandr A. Udod ◽  
Hanna S. Voronina ◽  
Olena Yu. Ivchenkova
Keyword(s):  
Neural Network ◽  
Dental Caries ◽  
Old Patients ◽  
Software Application ◽  
The Neural Network ◽  
Software Product ◽  
High Level ◽  
Network Technologies ◽  
Python Programming ◽  
And Training

The aim: of the work was to develop and apply in the clinical trial a software product for the dental caries prediction based on neural network programming. Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used. Results: The intensity of carious dental lesions was the highest in 35-44 years old patients – 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the “CariesPro” software application was created. Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

scholarly journals NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES

2019 ◽  
Vol 2 ◽  
pp. 141
Author(s):  
Krasimir Ognyanov Slavyanov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Synthetic Aperture ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Full Explanation ◽  
Network Method ◽  
High Level ◽  
And Training

This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of post-receive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images.

scholarly journals Prediction of fragmentation of kidney stones: A statistical approach from NCCT images

2016 ◽  
Vol 10 (7-8) ◽  
pp. 237 ◽  
Author(s):  
Krishna Moorthy ◽  
Meenakshy Krishnan
Keyword(s):  
Neural Network ◽  
Renal Stone ◽  
Statistical Approach ◽  
Region Of Interest ◽  
Stone Disease ◽  
True Negative ◽  
The Neural Network ◽  
Neural Network System ◽  
Percent Accuracy ◽  
And Training

<p><strong>Introduction:</strong> We sought to develop a system to predict the fragmentation of stones using non-contrast computed tomography (NCCT) image analysis of patients with renal stone disease.</p><p><strong>Methods:</strong> The features corresponding to first order statistical (FOS) method were extracted from the region of interest in the NCCT scan image of patients undergoing extracorporeal shockwave lithotripsy (ESWL) treatment and the breakability was predicted using neural network.</p><p><strong> Results:</strong> When mean was considered as the feature, the results indicated that the model developed for prediction had sensitivity of 80.7% in true positive (TP) cases. The percent accuracy in identifying correctly the TP and true negative (TN) cases was 90%. TN cases were identified with a specificity of 98.4%.</p><p><strong>Conclusions:</strong> Application of statistical methods and training the neural network system will enable accurate prediction of the fragmentation and outcome of ESWL treatment.</p>

Study on Data Security Technology of Neural Network System

2014 ◽  
Vol 701-702 ◽  
pp. 1041-1044
Author(s):  
Yan Wei Hong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Security Technology ◽  
Fusion Algorithm ◽  
Basic Principles ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
And Training

This paper analyzes the neural network algorithm model, introduces the basic principles and training process of BP neural network algorithm, analyzes the BP neural network weights adjustment processand the method of determining the number of nodes in each layer; in improved protocol algorithm basis LEACH-E, combined with the BP neural network algorithm, we propose a new data fusion algorithm BPDFA to reduce energy consumption to attain the network lifetime goal.

scholarly journals Analysis of selected methods of creating artificial intelligence on the example of a popular card game

2020 ◽  
Vol 16 ◽  
pp. 233-240
Author(s):  
Łukasz Gałka ◽  
Mariusz Dzieńkowski
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Execution Time ◽  
Card Game ◽  
Optimal Method ◽  
Statistical Algorithm ◽  
The Neural Network ◽  
Implementation Time ◽  
And Training ◽  
Selection Of

The aim of the article was to analyze selected methods of creating artificial intelligence in a popular card game. Two experiments were conducted: with a human and with a computer. The following algorithms were analyzed: random, min-max, based on a neural network, statistical and statistical with the use of "cheating" technique. The examined parameters were as follows: efficiency, execution time, number of implementation code lines, implementation time and training duration. The indicator with the greatest impact on the selection of the most optimal method was efficiency. The research has shown no difference in efficiency for the neural network-based algorithm and the statistical algorithm. In other cases, the differences in this feature were significant. The use of the "cheating" technique has increased the efficiency.

scholarly journals NEURAL NETWORK FORMATION OF EDUCATIONAL QUALIFICATION CHARACTERISTICS IN THE SYSTEM OF DISTANCE LEARNING MOODLE

1970 ◽  
Vol 14 (6) ◽  
Author(s):  
Olena O. Arsirii ◽  
Olena H. Zhylenko
Keyword(s):  
Neural Network ◽  
Distance Learning ◽  
Network Formation ◽  
Learning System ◽  
Educational Institutions ◽  
Educational Qualification ◽  
The Neural Network ◽  
Distance Learning System ◽  
Wide Range ◽  
And Training

The method for formation of the neural network based on educational qualification characteristics of graduates of educational institutions in distance learning system is offered. Modeling was performed by means of MOODLE, NeuroShell2 and PHP under the Code certification and training of seafarers' for seafarers training center. It is shown that the proposed method can be used for a wide range of educational institutions.

scholarly journals Foreign Trade Export Forecast Based on Fuzzy Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Liu
Keyword(s):  
Neural Network ◽  
Foreign Trade ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Training Algorithms ◽  
Whole Process ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
The Index System ◽  
And Training

This article first analyzes the significance and methods of foreign trade export forecasting and determines the index system of foreign trade export forecasting by analyzing the results of foreign trade export forecasting research at home and abroad. Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural network and fuzzy theory are combined to establish the prediction model. Finally, according to the characteristics of foreign trade exports, this article comprehensively considers the influence of various factors, applies the fuzzy neural network model to the foreign trade export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.

Nonimaging optical design with supporting quadric method and deep neural network

2021 ◽  
Vol 15 ◽  
Author(s):  
Hang Zhang ◽  
Peifu Ma ◽  
Jiawen Chen ◽  
Yuejiao Hu ◽  
Huahao Shou
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Design Method ◽  
Optical Design ◽  
Data Set ◽  
The Neural Network ◽  
Network Method ◽  
Freeform Optics ◽  
Sample Set ◽  
And Training

Background: The supporting quadric method(SQM) is a versatile method for designing freeform optics for desired irradiance redistribution, but the consumed time of solution optimization increases rapidly with the refinement of the mapping grid. Objective: As the complexity of light distribution is getting higher and higher, time-consuming will also increase exponentially. This paper proposes an idea of applying the deep neural network method to optical design. Methods: In this article, we established a special corresponding relationship and made a data set. Hand over to deep network learning and training. Finally, a hybrid design method of deep learning and optical design was realized and verified. Results: Compared with the traditional method, this method is more efficient. Here we use a deep neural network(DNN) to accelerate the freeform optical design. After the DNN was trained by a sample set consisting of a uniform pattern and eight different Chinese characters represented by an array with 11 × 11, it can generate a character's reflector within few milliseconds. Conclusion: As proof of this new method, a character pattern reflector was manufactured and tested, and the experimental irradiance distribution is closed to the expectation, which means the neural network has the excellent capability to memorize all of the learned characters. SQM combined with DNN has the potential to establish a particular “optical font library” and even offers a promising path for rapid freeform optical design to realize the function of “optical typography”.

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