A pre-radical basis function with deep back propagation neural network research

2014 ◽  
Author(s):  
Hui Wen ◽  
Weixin Xie ◽  
Jihong Pei
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Research

Research on Active Suspension System Based on BP and RBF Network Algorithm

2011 ◽  
Vol 230-232 ◽  
pp. 149-153 ◽  
Author(s):  
Chuan Yin Tang ◽  
Guang Yao Zhao ◽  
Yi Min Zhang ◽  
Xiao Yu E
Keyword(s):  
Neural Network ◽  
Basis Function ◽  
Degrees Of Freedom ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Rbf Network ◽  
Network Algorithm

A six degrees of freedom half body vehicle suspension system is presented in the paper .The Back Propagation neural network algorithm and the Radial-Basis Function network algorithm is adopted to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is obtained. A great deal of simulation work is done. Simulation results demonstrate that both the designed radius basis function neural network and the back propagation neural network work well for the proposed vehicle suspension model in the paper .

Application of Radial Basis Function Neural Network on the Prediction of Urban Built-Up Area

2014 ◽  
Vol 556-562 ◽  
pp. 5308-5311
Author(s):  
Li Hua Chen ◽  
Yu Chen Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quantitative Prediction ◽  
Basic Issue ◽  
Radial Basis ◽  
Neural Network Prediction ◽  
Network Prediction

The study on the prediction of urban built-up area is the basic issue in urban planning. This paper takes the prediction of urban built-up area of Hefei city as an example, building a factor system that affects built-up area from the economic, social and environmental dimensions. Then, the paper establishes a quantitative prediction model based on the Radial Basis Function neural network. As a comparison, the paper also uses the Back Propagation neural network to predict. The results show that the Radial Basis Function neural network prediction has a higher accuracy and the prediction result is more reasonable and reliable.

Estimation of ambiguous blast-induced ground vibration using intelligent models: A case study

2018 ◽  
Vol 49 (4) ◽  
pp. 147-157 ◽  
Author(s):  
Ragam Prashanth ◽  
DS Nimaje
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Basis Function ◽  
Back Propagation ◽  
Ground Vibration ◽  
Back Propagation Neural Network ◽  
Mean Square ◽  
Intelligent Models

Blasting is an economical and viable operation for reliable excavation of hard rock in mining and civil construction. An ambiguous ground vibration generated by blasting is unenviable and causes grievous damage to nearby inhabitants, residential premises, and other sensitive sites. Accordingly, the proper assessment of indistinct blast-induced ground vibration is a requisite to pinpoint the safe limits in and around mines. An endeavor has been made in this article to apply four predictive models, namely, support vector machine, feed forward back propagation neural network, cascaded forward back propagation neural network, and radial basis function neural network to estimate the ground vibration caused by blasting operation conducted at Mine-A, India. In this article, a total number of 121 blasting operations with relevant parameters are recorded. The most influential parameters of ground vibration are the number of holes, burden, spacing, hole diameter, hole depth, top stemming, maximum explosive charge per delay, and the distance from blast source, which were considered as input parameters. Ground vibration is measured in terms of peak particle velocity and is considered as output. The performance indicators of constructed network models were chosen as the coefficient of determination (R2), root mean square error, and variance account for. Among all constructed intelligent models, the radial basis function neural network with architecture 8-80-1 and R2 of 0.9918, root mean square error of 4.4076, and variance account for of 99.1800 was found to be optimum. Sensitivity analysis showed that the number of holes, burden, and top stemming are the most effective parameters leads to ground vibration due to blasting.

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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