scholarly journals Intelligent Prediction of Refrigerant Amounts Based on Internet of Things

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jincai Chang ◽  
Qiuling Pan ◽  
Zhihao Shen ◽  
Hao Qin
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Dimensionality Reduction ◽  
Rbf Neural Network ◽  
Network Models ◽  
Practical Significance ◽  
Data Sets ◽  
Effective Parameters ◽  
Neural Network Models ◽  
Refrigeration Unit

In a refrigeration unit, the amount of refrigerant has a substantial influence on the entire refrigeration system. To predict the amount of refrigerant in refrigerators with the best performance, this study used refrigerator data collected in real time via the Internet of Things, which were screened to include only the effective parameters related to the compressor and refrigeration properties (based on their practical significance and the research background) and cleaned by applying longitudinal dimensionality reduction and transverse dimensionality reduction. Then, on the basis of an idealized model for refrigerator data, a model of the relationships between refrigerant amount (the dependent variable) and temperature variation, refrigerator compartment temperature, freezer temperature, and other relevant parameters (independent variables) was established. A refrigeration model based on a neural network was then established for predicting the amount of refrigerant and was used to predict five unknown amounts of refrigerant from data sets. BP neural network and RBF neural network models were used to compare the prediction results and analyze the loss functions. From the results, it was concluded that the unknown amount of refrigerant was most likely to be 32.5 g. It is of great practical significance for refrigerator production and maintenance to study the prediction of the amount of refrigerant remaining in a refrigerator.

scholarly journals A data-driven neural network architecture for sentiment analysis

2019 ◽  
Vol 53 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Erion Çano ◽  
Maurizio Morisio
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Network Models ◽  
Data Sets ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Neural Network Models ◽  
Content Type ◽  
Max Pooling

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

A Confidence-Based RBF Neural Network Ensemble Learning Paradigm with Application to Delinquent Prediction for Credit Risk Management

2010 ◽  
pp. 105-117
Author(s):  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Credit Risk ◽  
Ensemble Learning ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Ensemble ◽  
Credit Risk Management ◽  
Learning Paradigm ◽  
Neural Network Models

In this study, a multistage confidence-based radial basis function (RBF) neural network ensemble learning model is proposed to design a reliable delinquent prediction system for credit risk management. In the first stage, a bagging sampling approach is used to generate different training datasets. In the second stage, the RBF neural network models are trained using various training datasets from the previous stage. In the third stage, the trained RBF neural network models are applied to the testing dataset and some prediction results and confidence values can be obtained. In the fourth stage, the confidence values are scaled into a unit interval by logistic transformation. In the final stage, the multiple different RBF neural network models are fused to obtain the final prediction results by means of confidence measure. For illustration purpose, two publicly available credit datasets are used to verify the effectiveness of the proposed confidence-based RBF neural network ensemble learning paradigm.

scholarly journals Econometric and RBF Neural Network Models for Analyzing Automobile Demand in Iran

2013 ◽  
Vol 6 (12) ◽  
pp. 2171-2180
Author(s):  
Hamed Pouralikhani ◽  
Alimohammad Kimiagari ◽  
Mohsen Keyvanloo ◽  
Hesamaddin Najmi
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Automobile Demand

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.

scholarly journals Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

2009 ◽  
Vol 13 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Thirunavukkarasu Ganapathy ◽  
Parkash Gakkhar ◽  
Krishnan Murugesan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Nox Emissions ◽  
Jatropha Oil ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

Comparing performance of MLP and RBF neural network models for predicting South Africa’s energy consumption

2012 ◽  
Vol 23 (3) ◽  
pp. 40-46 ◽  
Author(s):  
Olanrewaju A. Oludolapo ◽  
Jimoh A. Adisa ◽  
Kholopane A. Pule
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Rbf Neural Network ◽  
Close Association ◽  
Network Models ◽  
Industrial Sector ◽  
Percentage Error ◽  
Neural Network Models ◽  
Economic Development Plan ◽  
Artificial Neural Network Ann

In view of the close association between energy and economic growth, South Africa’s aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa’s industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model.

scholarly journals Summarize the Work Related to Prediction of Stock Market’s Option Prices with Artificial Intelligence using Standard Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 5023-5031
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Rbf Neural Network ◽  
Network Models ◽  
Energy Potential ◽  
Neural Network Models ◽  
The Neural Network ◽  
Bias Function

Forecasting and prediction are based on pattern recognition. It may be a human energy potential increase day today when he grownup a young guy, but afterward, his energy potential going downwards. So, we observed the pattern with the help of neural network models; these are radical bias function (RBP) and back-propagation (BP). Utilizing the neural network model, it also has many classification parts like a deep neural network, feedforward neural network, recurrent neural network, convolutional neural network and many more. In the forecasting or prediction, we have a large amount of data to manage. We trained the data with algorithm and here we also use the neural network models. We used optimization techniques that are inspired by biological swarm. Nowadays, lots of data generate day by day like market, medical, education, automobile, etc. we need recognition of the pattern for prediction of future expectations. That expectation of prediction very helpful and needy to gain profit of human beings. In this work, we use SOM (self-Organized Map), RBF (Radical Bias Function), DNN (Deep Neural Network) and PGO (Plant Grow Optimization). The total data point for the processing used 27500. The evaluation of the performance used standard parameters such as ET, MAE, MSE, RMSE and MI. The proposed algorithm implemented in MATLAB software. The cascaded neural network classifier is the combination of the SOM and RBF neural network models. The SOM neural network model proceeds the task of clustering and RBF neural network model used for prediction.

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