Application and Research of the Granular Evolutionary Neural Network Algorithms for the Complex Network

2013 ◽  
Vol 321-324 ◽  
pp. 2080-2084
Author(s):  
Yong Qin Tao ◽  
Ping Ding Zhang ◽  
Qing Li
Keyword(s):  
Neural Network ◽  
Complex Network ◽  
Quotient Space ◽  
Search Space ◽  
Equivalence Classes ◽  
Speed Of Convergence ◽  
Network Algorithms ◽  
Evolutionary Neural Network ◽  
The Neural Network ◽  
Input Variables

Research on an "Granular Evolutionary Neural Network Algorithms (GENNA)" is applied to the complex network. The theory of the granular quotient space is introduced to the neural network. At first input variables of the neural network are granulated to equivalence classes, so that the input variables of the network structure can be simplified, and have certain clustering characteristics and strong diversity, and then the network parameters and the weights are optimized using evolutionary algorithms, so as to avoid neural network to fall into the local extremum.The experimental results show that the algorithm effectively narrow the search space and accelerate the speed of convergence , and It is feasibility and effectiveness.

scholarly journals STUDY OF A MA16-BASED VERTEX TRIGGER FOR THE CDF EXPERIMENT

1995 ◽  
Vol 06 (05) ◽  
pp. 681-692
Author(s):  
R. ODORICO
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Impact Parameter ◽  
Electronic Components ◽  
The Neural Network ◽  
Viable Solution ◽  
Impact Parameters ◽  
Input Variables ◽  
Better Than

A Neural Network trigger for [Formula: see text] events based on the SVT microvertex processor of experiment CDF at Fermilab is presented. It exploits correlations among track impact parameters and azimuths calculated by the SVT from the SVX microvertex detector data. The neural trigger is meant for implementation on the systolic Siemens microprocessor MA16, which has already been used in a neural-network trigger for experiment WA92 at CERN. A suitable set of input variables is found, which allows a viable solution for the preprocessing task using standard electronic components. The response time of the neural-network stage of the trigger, including preprocessing, can be estimated ~10 μs. Its precise value depends on the quantitative specifications of the output signals of the SVT, which is still in development. The performance of the neural-network trigger is found to be significantly better than that of a conventional trigger exclusively based on impact parameter data.

Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction

2008 ◽  
pp. 2476-2493 ◽  
Author(s):  
David Encke
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Network Models ◽  
Linear Regression Models ◽  
Market Returns ◽  
Neural Network Models ◽  
The Neural Network ◽  
Variable Reduction ◽  
Input Variables ◽  
Using Data

Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market returns. Unfortunately, many of these studies fail to consider alternative forecasting techniques, or the relevance of the input variables. The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

scholarly journals The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

scholarly journals Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

2021 ◽  
pp. 19-29
Author(s):  
Ruyang Mo ◽  
Huihui Wang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Simulation Software ◽  
Network Algorithms ◽  
System Description ◽  
Software Interface ◽  
Operation Speed ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Rectification Process

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

2021 ◽  
Vol 6 (2) ◽  
pp. 128-133
Author(s):  
Ihor Koval ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Analysis Of Algorithms ◽  
Network Models ◽  
Data Sets ◽  
Neural Network Models ◽  
Network Algorithms ◽  
Modern Computer ◽  
The Neural Network

The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.

Neural networks for modelling the energy consumption of metro trains

2019 ◽  
Vol 234 (7) ◽  
pp. 722-733 ◽  
Author(s):  
Pablo Martínez Fernández ◽  
Pablo Salvador Zuriaga ◽  
Ignacio Villalba Sanchís ◽  
Ricardo Insa Franco
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Mean Square ◽  
The Neural Network ◽  
Train Speed ◽  
Input Variables ◽  
Trained Neural Network ◽  
Traction Power

This paper presents the application of machine learning systems based on neural networks to model the energy consumption of electric metro trains, as a first step in a research project that aims to optimise the energy consumed for traction in the Metro Network of Valencia (Spain). An experimental dataset was gathered and used for training. Four input variables (train speed and acceleration, track slope and curvature) and one output variable (traction power) were considered. The fully trained neural network shows good agreement with the target data, with relative mean square error around 21%. Additional tests with independent datasets also give good results (relative mean square error = 16%). The neural network has been applied to five simple case studies to assess its performance – and has proven to correctly model basic consumption trends (e.g. the influence of the slope) – and to properly reproduce acceleration, holding and braking, although it tends to slightly underestimate the energy regenerated during braking. Overall, the neural network provides a consistent estimation of traction power and the global energy consumption of metro trains, and thus may be used as a modelling tool during further stages of research.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

scholarly journals PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2150
Author(s):  
Romênia G. Vieira ◽  
Mahmoud Dhimish ◽  
Fábio M. U. de Araújo ◽  
Maria I. S. Guerra
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Detection ◽  
Machine Learning Techniques ◽  
Photovoltaic Systems ◽  
The Neural Network ◽  
Learning Techniques ◽  
Network Output ◽  
Input Variables ◽  
Power Point

This work introduces a new fault detection method for photovoltaic systems. The method identifies short-circuited modules and disconnected strings on photovoltaic systems combining two machine learning techniques. The first algorithm is a multilayer feedforward neural network, which uses irradiance, ambient temperature, and power at the maximum power point as input variables. The neural network output enters a Sugeno type fuzzy logic system that precisely determines how many faulty modules are occurring on the power plant. The proposed method was trained using a simulated dataset and validated using experimental data. The obtained results showed 99.28% accuracy on detecting short-circuited photovoltaic modules and 99.43% on detecting disconnected strings.

scholarly journals Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

2020 ◽  
Author(s):  
Milad Shayan ◽  
Mohammad Sabouri ◽  
Leila Shayan ◽  
Shahram Paydar
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Cause Of Death ◽  
Blood Gases ◽  
Blood Gas ◽  
Network Algorithms ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
The Neural Network ◽  
Golden Standard

ABSTRACTBackgroundTrauma is the third leading cause of death in the world and the first cause of death among people younger than 44 years. In traumatic patients, especially those who are injured early in the day, arterial blood gas (ABG) is considered a golden standard because it can provide physicians with important information such as detecting the extent of internal injury, especially in the lung. However, measuring these gases by laboratory methods is a time-consuming task in addition to the difficulty of sampling the patient. The equipment needed to measure these gases is also expensive, which is why most hospitals do not have this equipment. Therefore, estimating these gases without clinical trials can save the lives of traumatic patients and accelerate their recovery.MethodsIn this study, a method based on artificial neural networks for the aim of estimation and prediction of arterial blood gas is presented by collecting information about 2280 traumatic patients. In the proposed method, by training a feed-forward backpropagation neural network (FBPNN), the neural network can only predict the amount of these gases from the patient’s initial information. The proposed method has been implemented in MATLAB software, and the collected data have tested its accuracy, and its results are presented.ResultsThe results show 87.92% accuracy in predicting arterial blood gas. The predicted arterial blood gases included PH, PCO2, and HCO3, which reported accuracy of 99.06%, 80.27%, and 84.43%, respectively. Therefore, the proposed method has relatively good accuracy in predicting arterial blood gas.ConclusionsGiven that this is the first study to predict arterial blood gas using initial patient information(systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), and age), and based on the results, the proposed method could be a useful tool in assisting hospital and laboratory specialists, to be used.

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