scholarly journals BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

2019 ◽  
Vol 95 ◽  
pp. 04008
Author(s):  
Gao Kun ◽  
Wang Aimin ◽  
Ge Yan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Main Trend ◽  
Intelligent Fault Diagnosis ◽  
The Neural Network ◽  
Training Samples

Intelligent diagnosis is the main trend of modern fault diagnosis technology. The emergence of artificial neural network technology provides a new way for this kind of intellectualization. Aiming at the problem of microwave module fault diagnosis, an intelligent fault diagnosis method based on BP(Back Propagation) neural network is proposed in this paper. In this paper, the process of determining the neural network model and the operation flow of BP algorithm are introduced, and the network is trained with training samples. By applying the neural network model to an AQ module for testing, the feasibility, accuracy and efficiency of the fault diagnosis of the microwave module are verified, which provides a new method for intelligent fault diagnosis of this kind of microwave module.

scholarly journals Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

2019 ◽  
Vol 9 (2) ◽  
pp. 3600-3607
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Filtering ◽  
Commercial Success ◽  
The Neural Network ◽  
Proposed Model

Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India.

Research of Gasoline Engine Ignition Control Based on Neural Network

2009 ◽  
Vol 626-627 ◽  
pp. 501-504
Author(s):  
Y.Y. Wang ◽  
Jian Guo Yang ◽  
B.Y. Song
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Performance ◽  
Gasoline Engine ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
The Neural Network

In order to realize the precise ignition control of gasoline engine, an ignition advance angle BP (Back Propagation) neural network model is built. The improved LM (Levenberg-Marquardt) learning algorithm is used in the model to increase the neural network performance. The neural network model is trained and tested by matlab program. For a variety of inputs, the trained ignition advance angle neural network can carry out correct outputs. Compared with the experimental ignition advance angle, the maximum error of the neural network ignition advance angle is less than 5%. Compared with the experimental map method, the ignition advance angle neural network has the advantage of online modifying the value of ignition advance angle, so it can make the gasoline engine acquire the best ignition advance angle on various working conditions. The results show that the ignition advance angle neural network model established in this paper is effective and accurate. The performance of gasoline engine can be improved ultimately.

scholarly journals Back-propagation neural network model to predict visibility at a road link-level

2020 ◽  
Vol 8 ◽  
pp. 100250
Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Ajinkya S. Mane ◽  
Christopher Godfrey
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network

Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Supplier Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Supplier Evaluation ◽  
Test Results ◽  
Model Training

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

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