Magnitude and phase determination of harmonic currents by adaptive learning back-propagation neural network

1999 ◽  
Author(s):  
M. Rukonuzzaman ◽  
M. Nakaoka
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Phase Determination ◽  
Harmonic Currents

Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining

2014 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Rajesh Kumar Porwal ◽  
Vinod Yadava ◽  
J. Ramkumar
Keyword(s):  
Neural Network ◽  
Removal Rate ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Performance

Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material.

Determination of Gamma point source efficiency based on a back-propagation neural network

2018 ◽  
Vol 29 (5) ◽  
Author(s):  
Hong-Long Zheng ◽  
Xian-Guo Tuo ◽  
Shu-Ming Peng ◽  
Rui Shi ◽  
Huai-Liang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Point Source ◽  
Back Propagation ◽  
Back Propagation Neural Network

A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings

2004 ◽  
Vol 56 (3) ◽  
pp. 147-157 ◽  
Author(s):  
Yuan Kang ◽  
Ping‐Chen Shen ◽  
Cheng‐Hsien Chen ◽  
Chih‐Ching Huang ◽  
Lin‐Kan Yang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Order ◽  
Operating Conditions ◽  
Ball Bearings ◽  
Deep Groove ◽  
Variable Function

All deep‐groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high‐order and coupled‐variable function. This paper has verified that the stiffness function for all deep‐groove ball bearings can be replaced by a back‐propagation neural network (BPNN) which is trained by using some (not all) samples.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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