Research of Gasoline Engine Ignition Control Based on 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.

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BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

E3S Web of Conferences ◽

10.1051/e3sconf/20199504008 ◽

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.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7702.129219 ◽

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.

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Stock Market Prediction Using Artificial Neural Networks

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.1055 ◽

2012 ◽

Vol 6-7 ◽

pp. 1055-1060 ◽

Cited By ~ 4

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.

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