scholarly journals Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network

10.5772/16191 ◽  
2011 ◽  
Author(s):  
Chonghai Xu ◽  
Jingjie Zhang ◽  
Mingdong Yi
Keyword(s):  
Neural Network ◽  
Optimum Design ◽  
Back Propagation ◽  
Composite Ceramic ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Die Materials ◽  
Design And Application

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: II. with Hybrid GA-BP Method

2010 ◽  
Vol 154-155 ◽  
pp. 1091-1095
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Process Design ◽  
Hot Pressing ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Ceramic Tool ◽  
Die Materials ◽  
Die Material ◽  
Two Hybrid

In this paper, the two hybrid algorithms of back propagation artificial neural network and genetic algorithm were used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. Compared with the BP algorithm, the predicted results of hybrid algorithm indicated that the combination algorithm can offer a robust and efficient way for the fabrication process design of ceramic tool and die materials.

The Hot Pressing Process Optimization of Ti(C, N) Matrix Nano-Composite Ceramic Die Materials with the Back Propagation Neural Network and Immune Genetic Algorithm

2013 ◽  
Vol 652-654 ◽  
pp. 2086-2092
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Hui Fa Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fracture Toughness ◽  
Flexural Strength ◽  
Hot Pressing ◽  
Back Propagation ◽  
Composite Ceramic ◽  
Back Propagation Neural Network ◽  
Immune Genetic Algorithm ◽  
Nano Composite

The two hybrid algorithms of back propagation neural network and immune genetic algorithm were used in the optimum design of the hot pressing parameters of Ti(C, N) matrix nano-composite ceramic die material. The BP algorithm could set up the relationship well between the hot pressing parameters and single mechanical property. Compared with the experimental value, the relative error of fracture toughness, hardness and flexural strength is only 4.65%, 0.23% and 4.05%, respectively. After analyzed the predicted results, the best predicted results were the sintering temperature was about 1455°C and the holding time was 11 min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

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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