Development of Back Propagation Neural Network (BPNN) Model to Predict Combustion Parameters of Diesel Engine

2016 ◽  
pp. 71-83
Author(s):  
M. Shailaja ◽  
A. V. Sita Rama Raju
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bpnn Model

A comparative study of different prediction models in predicting human resource demands

2021 ◽  
pp. 1-11
Author(s):  
Bo Huang
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Comparative Study ◽  
Human Resource ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Methods ◽  
Bpnn Model ◽  
Good Agreement

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

scholarly journals Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 1693
Author(s):  
Yu Liu ◽  
Miaomiao Li ◽  
Peifeng Su ◽  
Biao Ma ◽  
Zhanping You
Keyword(s):  
Neural Network ◽  
Granular Material ◽  
Granular Materials ◽  
Particle Shape ◽  
Back Propagation ◽  
Discrete Element ◽  
Back Propagation Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bpnn Model

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

scholarly journals Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 349-360 ◽  
Author(s):  
Zhiyuan Wu ◽  
Changbo Jiang ◽  
Mack Conde ◽  
Bin Deng ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
The North ◽  
Mode Decomposition ◽  
Bpnn Model

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights. An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

Temperature Compensation System of Diesel Engine Piezoresistive Pressure Transducer Based on Neural Networks and LabVIEW

2012 ◽  
Vol 241-244 ◽  
pp. 833-836
Author(s):  
Zhao Jing Tong ◽  
Xiu Hua Shi ◽  
Xiang Dang Du ◽  
Sheng Wu Wang ◽  
Tian Peng He
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Temperature Compensation ◽  
Pressure Transducer ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Compensation System ◽  
Calibration Data ◽  
Neural Network Algorithm ◽  
Labview Platform

The work presented in this paper focuses on the output of temperature compensation system of diesel engine piezoresistive pressure transducer, it is nonlinear and influenced easily by environmental temperature. The paper studied the method of using improved neural network algorithm based on LabVIEW and MATLAB to increase the precision and efficiency of pressure detection. In order to eliminate the temperature influence, this paper proposed the method of using improved back propagation neural network on LabVIEW platform, which realized the communication between LabVIEW and MATLAB. The experimental results show that, with the using of Matlab Script node, the system benefits from the huge computing power of MATLAB to train the network based on the static calibration data of transducer, and makes temperature compensation more efficient and accurate.

Prediction for Air-Bending Springback Radius of Sheet Metal Using Back Propagation Neural Network and Micro Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1174-1177
Author(s):  
Ze Min Fu ◽  
Guang Ming Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sheet Metal ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Accurate Prediction ◽  
Air Bending ◽  
Micro Genetic Algorithm ◽  
Bpnn Model

Springback radius is a very important factor to influence the quality of sheet metal air-bending forming. Accurate prediction of springback radius is essential for the design of air-bending tools. In this paper, a three-layer back propagation neural network (BPNN), integrated with micro genetic algorithm (MGA), is proposed to solve the problem of springback radius. A micro genetic algorithm is used for minimizing the error between the predictive value and the experimental one. Based on air-bending experiment, the prediction model of springback radius is developed by using the integrated neural network. The results show that more accurate prediction of springback radius can be obtained with the MGA-BPNN model. It can be taken as a valuable tool for air-bending forming of sheet metal.

scholarly journals A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2808
Author(s):  
Li Li ◽  
Jiahui Yu ◽  
Hang Cheng ◽  
Miaojuan Peng
Keyword(s):  
Neural Network ◽  
Body Temperature ◽  
Temperature Measurement ◽  
Error Compensation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Least Square Regression ◽  
Bpnn Model

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

Research of Marine Diesel Engine’s State Prediction Based on Evolutionary Neural Network and Spectrometric Analysis

2011 ◽  
Vol 346 ◽  
pp. 339-345 ◽  
Author(s):  
Hai Jun Wei ◽  
Guo You Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Diesel Engine ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lubricating Oil ◽  
Spectrometric Analysis ◽  
Marine Diesel Engine ◽  
Marine Diesel

In this paper, an evolutionary neural networks model is proposed to predict the content of metal elements contained in marine diesel engine lubricating oil, by fusing genetic algorithms (GAs) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. The input data of metal content was detected by spectrometric analysis. Genetic algorithms are used to globally optimize the weights and threshold of BP neural networks. Moreover, one case study was presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of only BPNN method to illustrate the feasibility and effectiveness of the proposed method. The relative error on average of results is 1.52%, it can meet the precision request of state detecting in marine diesel engine.

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