Evaluation on Training Algorithms of Back Propagation Neural Network for a Solar Photovoltaic Based DSTATCOM System

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

ICTACT Journal on Soft Computing ◽

10.21917/ijsc.2016.0159 ◽

2016 ◽

Vol 06 (02) ◽

pp. 1144-1152 ◽

Cited By ~ 1

Author(s):

Jency Paulin B. ◽

◽

Praynlin E. ◽

Keyword(s):

Neural Network ◽

Output Power ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Solar Photovoltaic ◽

Power Forecasting

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

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

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An Effective Customer Churn Prediction Model using Adaptive Gain with Back Propagation Neural Network in Cloud Computing Environment

Journal of Research on the Lepidoptera ◽

10.36872/lepi/v51i1/301034 ◽

2020 ◽

Vol 51 (1) ◽

pp. 386-399

Author(s):

JEYAKARTHIC M

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Prediction Model ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Churn Prediction ◽

Computing Environment ◽

Cloud Computing Environment ◽

Customer Churn ◽

Customer Churn Prediction

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

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

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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