scholarly journals A Novel Nonlinear Function Fitting Model Based on FOA and GRNN

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Zhang ◽  
Jianguang Niu ◽  
Sanggyun Na
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Back Propagation ◽  
Nonlinear Function ◽  
Step Length ◽  
Small Samples ◽  
Main Function ◽  
Function Fitting ◽  
Griewank Function ◽  
Searching Method

The nonlinear function fitting is an essential research issue. At present, the main function fitting methods are statistical methods and artificial neural network, but statistical methods have many inherent strict limits in application, and the back propagation (BP) neural network used widely has too many optimized parameters. For the gaps and lacks of existing researches, the FOA-GRNN was proposed and compared with the GRNN, GA-BP, PSO-BP, and BP through three nonlinear functions from simplicity to complexity for verifying the accuracy and robustness of the FOA-GRNN. The experiment results showed that the FOA-GRNN had the best fitting precision and fastest convergence speed; meanwhile the predictions were stable and reliable in the Mexican Hat function and Rastrgrin function. In the most complex Griewank function, the prediction of FOA-GRNN was becoming unstable and the model did not show better than GRNN model adopting equal step length searching method, but the performance of FOA-GRNN is superior to that of GA-BP, PSO-BP, and BP. The paper presents a new approach to optimize the parameter of GRNN and also provides a new nonlinear function fitting method, which has better fitting precision, faster calculation speed, more few adjusted parameters, and more powerful processing ability for small samples. The processing capacity of FOA for treating high complex nonlinear function needs to be further improved and developed in the future study.

Applying Back-Propagation Neural Network for Estimating the Slump of Concrete

2013 ◽  
Vol 651 ◽  
pp. 986-989
Author(s):  
Chin Ming Kao ◽  
Li Chen ◽  
Chang Huan Kou ◽  
Shih Wei Ma
Keyword(s):  
Neural Network ◽  
High Performance ◽  
High Performance Concrete ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Nonlinear Function ◽  
Multiple Linear Regression Analysis ◽  
Back Propagation Neural Network ◽  
Fine Aggregate ◽  
Furnace Slag

This paper proposes the back-propagation neural network (BPN) and applies it to estimate the slump of high-performance concrete (HPC). It is known that HPC is a highly complex material whose behaviour is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The results show that BPN obtains a more accurate mathematical equation through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.

A Modified Back Propagation Algorithm of Neural Network with Global Optimization

2014 ◽  
Vol 1042 ◽  
pp. 232-238
Author(s):  
Jing Jing Li ◽  
Zhe Cui
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Efficient Global Optimization ◽  
Back Propagation Algorithm ◽  
Global Optimization Algorithm ◽  
Function Fitting ◽  
Neural Network Algorithm

The advantages and weakens of traditional BP algorithm is briefly analyzed and an efficient global optimization algorithm is proposed.The basic principle of the algorithm is presented,and a new BP neural network algorithm based on the existing BP algorithm and the new global optimization algorithm is proposed, considering the new global optimization algorithm can solve the problem of local minimum efficiently. To verify the effectiveness of the new BP algorithm,the paper compared the experimental results of various algorithms in solving function fitting problem.

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

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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.

Sign in / Sign up

Export Citation Format

Share Document