Evaluation Method on Innovation Ability of Industrial Clusters: Based on Improved Back Propagation (BP) Neural Networks

2009 ◽  
Author(s):  
Guohong Chen ◽  
Kai Li
Keyword(s):  
Neural Networks ◽  
Evaluation Method ◽  
Back Propagation ◽  
Industrial Clusters ◽  
Bp Neural Networks ◽  
Innovation Ability

Study on Pilot Performance Model

2011 ◽  
Vol 383-390 ◽  
pp. 2545-2549
Author(s):  
Wei Liu ◽  
Cheng Kun Liu ◽  
Da Min Zhuang ◽  
Zhong Qi Liu ◽  
Xiu Gan Yuan
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Evaluation Method ◽  
Back Propagation ◽  
Performance Model ◽  
Experimental Comparison ◽  
Bp Neural Networks ◽  
Training Set ◽  
Pilot Performance ◽  
Movement Data

In order to evaluate pilot performance objectively, back propagation (BP) neural network model of 621423 form in topology with eye movement data was established. Data source of BP neural networks that came from former experiment and random interpolation was divided into training set and test set and normalized. Based on neural networks toolbox in Matlab, hidden layer nodes of BP networks were determined with empirical formula and experimental comparison ; BP algorithms in the toolbox were optimized; The training set data and test data were input into model for training and simulation; Pilot performance of the three skill levels was predicated and evaluated. The research shows that pilot performance can be accurately evaluated by setting up BP neural networks model with eye movement data and the evaluation method can provide a reference for flight training.

Improved Artificial BP Neural Networks and Genetic Algorithm Used to Predict the Purity of the Artificial Synthetic Hydrotalcite

2007 ◽  
Vol 280-283 ◽  
pp. 495-498
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Prediction Model ◽  
Process Parameters ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Raw Material ◽  
Algorithm Analysis ◽  
Neural Net ◽  
Bp Neural Networks

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on improved artificial back-propagation (BP) neural networks is developed. And the non-linear relationship between the hydrotalcite purity and the raw material adding amount of NaOH, MgCl2 and AlCl3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts. Moreover, the best processing technology is optimized using the genetic algorithm.

Automatic Cognitive Load Classification Using High-Frequency Interaction Events

2013 ◽  
Vol 9 (3) ◽  
pp. 73-88 ◽  
Author(s):  
Tao Lin ◽  
Xiao Li ◽  
Zhiming Wu ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Cognitive Load ◽  
High Frequency ◽  
Evaluation Method ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Selection Methods ◽  
Learning Techniques ◽  
Frequency Interaction

There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and in real-time evaluate cognitive load of user based the data. The study explores the possibility of using the features extracted from high-frequency interaction events to evaluate cognitive load to respond to the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) demonstrating the use of combining machine learning techniques and the HFI features in automatically evaluating cognitive load; (2) showing the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.

Application of BP Neural Networks in Damage Prediction of Multilayer Medium Penetration and Blasting

2012 ◽  
Vol 446-449 ◽  
pp. 1417-1420
Author(s):  
Xiao Ling Liu ◽  
Ting Lei ◽  
Yong Yao
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Bp Neural Networks ◽  
Bp Network ◽  
Damage Prediction ◽  
Multilayer Medium ◽  
Feed Forward Network ◽  
The Neural Networks

Back-propagation method (BP method) is the supervised learning algorithm that is the most widely and successfully used in feed forward network nowadays. This paper dealt with the penetration and blasting experimental data by BP Neural networks, including of the influence of the velocity and attack angles to damage of multilayer medium penetration and blasting. Through handling of the experimental data by the BP Network system, coupled effects of quantity of explosive and buried depth can be uncoupled. The curves of infundibular crater radius vs. quantity of explosive and infundibular crater depth vs. buried depth of explosive was given. Base on computing results, it is shown that the neural networks method can be used to predict the damage of multilayer medium penetration and blasting.

Prediction of the Forest Health Based on BP Neural Networks

2013 ◽  
Vol 726-731 ◽  
pp. 4303-4306 ◽  
Author(s):  
Yong Wang ◽  
Zhuang Xiong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forest Health ◽  
Bp Neural Networks ◽  
Forest Disease ◽  
Bp Neural Network Model

This paper simple introduced back propagation (BP) neural networks, and constructed a dynamic predict model, based on it to predict forest disease and insect and rat pest. Then it analyzed and simulated with the BP neural network model with the data produced in the recent ten years. The result indicated that the BP neural network model is reliable for predicting the forest disease and insect and rat pest. The method provides scientific foundation for the forestry management of studied area.

Using High-Frequency Interaction Events to Automatically Classify Cognitive Load

2015 ◽  
pp. 210-228
Author(s):  
Tao Lin ◽  
Zhiming Wu ◽  
Yu Chen
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Cognitive Load ◽  
Real Time ◽  
High Frequency ◽  
Evaluation Method ◽  
Back Propagation ◽  
Selection Methods ◽  
Back Propagation Neural Networks ◽  
Frequency Interaction

There is still a challenge of creating an evaluation method which can not only unobtrusively collect data without supplement equipment but also objectively, quantitatively and real-time evaluate cognitive load of users based the data. The study explores the possibility of using the features extracted from high-frequency interaction (HFI) events to evaluate cognitive load to respond the challenge. Specifically, back-propagation neural networks, along with two feature selection methods (nBset and SFS), were used as the classifier and it was able to use a set of features to differentiate three cognitive load levels with an accuracy of 74.27%. The main contributions of the research are: (1) knowledge about what detailed features may be predictive of cognitive load changes; (2) demonstrating the potential of using the HFI features in discriminating different cognitive load when suitable classifier and features are adopted.

Optimizing BP Networks by Means of Genetic Algorithms in Quality of Laser Milling

2013 ◽  
Vol 690-693 ◽  
pp. 3338-3342
Author(s):  
Zhao Mei Xu ◽  
Zong Hai Hong ◽  
Gang Yang ◽  
Qing An Wang
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Back Propagation ◽  
Maximum Relative Error ◽  
Bp Neural Networks ◽  
Technical Parameters ◽  
Evolutionary Neural Network ◽  
Laser Milling ◽  
Real Value ◽  
Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser milling. The prediction model of surface quality in laser milling parts, including the width, depth of cladding layer, was proposed based on the back propagation (BP) neural networks. The model combined the global optimization searching performance of the genetic algorithm and the localization of the back propagation (BP) neural networks. Five technical parameters were selected to test the reliability of the model. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2. 21% between the predicted content and the real value.

scholarly journals Thermal Error Prediction and Compensation of YK3610 Hobbing Machine Based on BP Neural Networks

2015 ◽  
Vol 9 (1) ◽  
pp. 678-681 ◽  
Author(s):  
Qianjian Guo ◽  
Rufeng Xu ◽  
Xiaoni Qi
Keyword(s):  
Neural Networks ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Thermal Error ◽  
Test System ◽  
Minimum Problem ◽  
Bp Neural Networks ◽  
Thermal Errors ◽  
Gear Hobbing ◽  
Thermal Sources

In this study, error compensation technology was proposed to reduce thermal errors of a gear hobbing machine, and one experiment was carried out to verify the compensation effect. Different thermal sources were used as modeling variables, and a prediction model of thermal errors was presented based on back propagation (BP) neural networks. In order to solve local minimum problem of BP neural networks, ant colony algorithm was used for training its link weights. Finally, one test system was developed based on the presented model, and an experiment was fulfilled. The result shows that prediction performance of the model is very well, and the residual error is less than 5 μm after compensation.

scholarly journals Beetle antennae search for neural network model with application to population prediction: An intelligent optimization algorithm

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4937-4952
Author(s):  
Qing Wu ◽  
Jie Wang ◽  
Gang Xu ◽  
Shuai Li ◽  
Dechao Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Bp Neural Networks ◽  
Intelligent Optimization ◽  
Intelligent Optimization Algorithm ◽  
Population Prediction

Traditional back-propagation (BP) neural networks can implement complex nonlinear mapping relationships, and solve internal mechanism problems. However, as number of samples increases, training BP neural networks may consume a lot of time. For this reason, to improve the efficiency as well as prediction accuracy of the neural network model, in this paper, we propose an intelligent optimization algorithm, by leveraging the beetle antennae search (BAS) strategy to optimize the weights of neural network model, and apply it to the population prediction. A series of experiments demonstrate the improved accuracy of the proposed algorithm over BP neural networks. In particular, the calculation time spent of neural network model via the proposed algorithm is only 20% of the one of BP neural network model. Finally, we present a reasonable trend of population growth in China, and analyze the causes of changes in population trends, which may provide an effective basis for the department to adjust population development strategies

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