scholarly journals Isobaric vapour-liquid equilibrium calculations of binary systems using neural network

2004 ◽  
Vol 69 (8-9) ◽  
pp. 669-674 ◽  
Author(s):  
Mehmet Bilgin
Keyword(s):  
Neural Network ◽  
Binary Systems ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Liquid Equilibrium ◽  
Chemical Structures ◽  
Feed Forward Back Propagation ◽  
The Neural Network ◽  
Classical Models ◽  
Vle Data

A model on a feed forward back propagation neural network was employed to calculate the isobaric vapour?liquid equilibrium (VLE) data at 40, 66.67, and 101.32 ??0.02 kPa for the methylcyclohexane ? toluene and isopropanol ? methyl isobutyl ketone binary systems, which are composed of different chemical structures (cyclic, aromatic, alcohol and ketone) and do not show azeotrope behaviour. Half of the experimental VLE data only were assigned into the designed framework as training patterns in order to estimate the VLE data over the whole composition range at the mentioned pressures. The results were compared with the data calculated by the two classical models used in this field, the UNIFAC and Margules models. In all cases the deviations the experimental activity coefficients and those calculated by the neural network model (NNET) were lower than those obtained using the Margules and UNIFAC models.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

Neural Network-based Evaluation of the Effect of the Motivation of Hospital Employees on Patients’ Satisfaction

2010 ◽  
Vol 5 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Payam Hanafizadeh ◽  
Neda Rastkhiz Paydar ◽  
Neda Aliabadi
Keyword(s):  
Neural Network ◽  
Organizational Performance ◽  
Back Propagation ◽  
Employee Motivation ◽  
Back Propagation Neural Network ◽  
Distributed Data ◽  
Hospital Employees ◽  
The Neural Network ◽  
Using Data ◽  
Network Studies

This article evaluates the effect of the motivation of employees on organizational performance using a neural network. Studies show that employee motivation influences organizational performance, particularly in organizations providing services. Methods based on statistical computations like regression and correlation analysis were used to measure the mutual effects of these factors. As these statistical methods necessitate the fulfillment of certain requirements like normally distributed data and because they are not able to express non-linear relations and hidden complicated patterns, a back propagation neural network has been used. The neural network was trained by using data from 300 questionnaires answered by hospital employees and 1933 patients hospitalized in a private hospital in Tehran over three successive months.

Research of Air Traffic Flow Forecasts Based on BP Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2912-2915
Author(s):  
Ming Qiang Chen ◽  
Jun Hong Feng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Back Propagation ◽  
Good Method ◽  
Back Propagation Neural Network ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Neural Network ◽  
Air Traffic Flow

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by back propagation neural network.

FFBPNN-BASED HIGH DROWSINESS CLASSIFICATION USING EMG AND WPT

2020 ◽  
Vol 32 (03) ◽  
pp. 2050023 ◽  
Author(s):  
Mousa Kadhim Wali
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Road Accidents ◽  
Mean Values ◽  
Physiological Signal ◽  
Feed Forward Back Propagation ◽  
Emg Signal ◽  
Amplitude Level

The detection of drowsiness level is important because it is the main reason for fatal road accidents. Electromyography of the upper arm and shoulder is an important physiological signal affected by drivers’ drowsiness, in which its amplitude level and frequency band of the sleep-deprived case are different than those of the alert state. Therefore depending on electromyography (EMG), its drowsiness frequency (80–100[Formula: see text]Hz) was detected in order to determine high drowsiness state based on wavelet packet transform (WPT) which decomposes the EMG signal into its approximation and detail coefficients up to level 4 using db2, db7, sym5 and coif5 wavelets. In this research after extraction, the two higher order statistical features, kurtosis and skewness, are computed from 3[Formula: see text]s window of the three EMG channels, and analysis of variance test is used to check whether their mean values are different for the different classes as both [Formula: see text]-values are less than 0.005 under db2 wavelet. Therefore, they were supplied to feed forward back propagation neural network (FFBPNN) as this type of neural network is used for distinguishing and classification purposes for different objects. They obtained an accuracy of 75% for detecting high levels among other levels of normal and low drowsiness with an average sensitivity of 78.63% and specificity of 75.97% because the spectrum of the EMG alert (non-drowsiness) signal of 80–100 Hz is different from that of drowsy 80–90[Formula: see text]Hz and high drowsy 78–95[Formula: see text]Hz signals.

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