Risk Warning of High-Tech Enterprise Independent Innovation Based on Chaotic Neural Network

2013 ◽  
Vol 568 ◽  
pp. 179-185
Author(s):  
Zhi Yong Wu ◽  
Hong Mei Chen ◽  
Xiu Hui Qi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Evaluation Index System ◽  
High Tech ◽  
Independent Innovation ◽  
Innovation Activities ◽  
Network Training ◽  
Risk Warning ◽  
Warning Model

Risk warning evaluation index system of independent innovation is established according to the process of innovation activities of high-tech enterprise, Chaotic Analysis Method is introduced into the BP(Back Propagation)Neural Network Model to research the early risk warning of high-tech enterprises independent innovation, the empirical results show that the integration of early warning model is feasible and effective, and significantly improve the convergence speed of network training, to some extent, avoid getting into local minimum.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 526
Author(s):  
Yi Lei ◽  
Xiaodong Qiu
Keyword(s):  
Neural Network ◽  
Index System ◽  
Evaluation System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Entropy Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Investment Climate ◽  
Cross Border

China’s cross-border e-commerce will usher in a new golden age of development. Based on seven countries which include the Russian Federation, Mongolia, Ukraine, Kazakhstan, Tajikistan, Kyrgyzstan and Belarus along the “Belt and Road”, an evaluation system for cross-border e-commerce investment climate indicators is established in this study. This research applied the entropy method twice to evaluate the investment climate of seven countries based on 5 years panel data comprehensively and these countries are then classified into politics-oriented and industry-oriented countries, and then the weight of indicators for each category is analyzed. In addition, cross-border e-commerce investors are proposed to prioritize industry-oriented countries. Back propagation neural network algorithm is used to map the existing data and optimize the evaluation index system in combination with the genetic algorithm. This research denotes the effort to find out the index evaluation combination corresponding to the best overall score, make the established evaluation index system applicable to other countries, and provide reference for cross-border e-commerce investors when evaluating the investment climate in each country. This study provides the important practical implications in the sustainable development of China’s cross-border e-commerce environment.

The Defect Classification of TFT-LCD Array Photolithography Process via Using Back-Propagation Neural Network

2013 ◽  
Vol 378 ◽  
pp. 340-345
Author(s):  
Shih Feng Chen ◽  
Chin Chih Lai
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Defect Classification ◽  
Large Area ◽  
Fifth Generation ◽  
Network Training ◽  
Optical Inspection ◽  
Photolithography Process

This research is conducted mainly by using the Auto Optical Inspection (AOI) in the fifth generation TFT-LCD factory. In the development of detect-classification system, we designed the back-propagation neural network which combined with Visual Basic as the interface and MATLAB as an image-processing tool. The system is able to determine and display the detected results. The defect classification mainly designed to detect and classify the following defects: the second layer of the photo resist residue (AS-Residue), the second layer of large-area photo resist residue (AS-BPADJ), and the third layer of photo resist residue (M2-residue) in the Array Photolithography Process. Finally, the result is shown the fact that without the complicated processing procedures, the four defects in the TFT-LCD Array Photo Process can be precisely and quickly classified by imaging processing and back-propagation neural network training. As result, it is feasible to reduce the costs and the risk of human judgments.

Calculation Method of Intermediate Bearing Displacement Value for Multisupported Shafting Based on Neural Network

2020 ◽  
pp. 1-7
Author(s):  
Deng Yibin ◽  
Yang Xiaogang ◽  
Huang Yanling ◽  
Pan Tian ◽  
Zhu Hanhua
Keyword(s):  
Neural Network ◽  
Mutual Influence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Training ◽  
The Neural Network ◽  
Element Simulation ◽  
Training Samples ◽  
Calculation Results ◽  
Classification Society

The mutual influence between the bearings of a ship's multisupport shafting makes its installation and alignment very difficult. This article addresses the problem of the calculation of the precise displacement value of each intermediate bearing and proposes a method for fitting the shafting characteristic function by using the GA-BP (genetic algorithm-back propagation) neural network. The neural network uses the intermediate bearing reaction as input to calculate the theoretical height of the bearing, thereby accurately calculating the displacement value. Taking the installation and alignment of a ro-ro ship's propulsion shafting as an application example, a neural network of the ship's shafting is established with training samples based on finite element simulation, and the effect of network training is discussed. The accuracy of the method is verified by a comparative analysis with the measured data of the ship's shafting. The calculation results of this method are used as a guide for the installation and alignment of the ship's shafting and have passed the delivery inspection of the classification society.

scholarly journals Pemodelan Sistem dengan Metode Neural Network Back Propagation Modeling System Using Neural Network Backpropagation

2016 ◽  
Vol 11 (2) ◽  
pp. 22
Author(s):  
Dwi Sudarno Putra ◽  
Toto Sugiarto ◽  
Meri Azmi
Keyword(s):  
Neural Network ◽  
Mathematical Formulation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Process ◽  
Propagation Modeling ◽  
Network Training ◽  
The Neural Network ◽  
Formulation Analysis ◽  
Method Analysis

A system can be modeled using mathematical formulation analysis method. Analysis of the system starts from the value of inputs, processes, noise, and output processes. then sought mathematical approach. This method is very complex, especially for some of the processes that have a higher order. In this article the author tries to discuss a method to perform modeling on a system by using Back Propagation Neural Network method.In the neural network method we require the initial data for network training process. Network training process is intended to gain weight. The weight is then identified with a model system. This paper has demonstrated that the modeling system can be done by the method of back propagation neural network 

scholarly journals Identifikasi Tahu Berformalin dengan Electronic Nose Menggunakan Jaringan Syaraf Tiruan Backpropagation

2016 ◽  
Vol 6 (2) ◽  
pp. 211
Author(s):  
Wida Astuti ◽  
Danang Lenono ◽  
Faizah Faizah
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Volatile Compounds ◽  
Electronic Nose ◽  
Back Propagation ◽  
Extraction Process ◽  
Back Propagation Neural Network ◽  
Differential Method ◽  
Gas Sensor Array ◽  
Network Training

During this time to identify pure and formalin tofu based on color and aroma involving human taster. But this tofu tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of tofu is determined by volatile compounds such as heksanal, ethanol, and 1-hexanol, while aroma of formalin tofu is determined by volatile compounds such as OH, CO, and hydrocarbon. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the back propagation neural network training to obtain optimal parameters. The parameters have been optimized is then tested on a random tofus. Based on test results, ANN-BP can identify samples with 100% accuracy rate so that the identification of a pure tofu and tofu formalin with electronic nose using back propagation neural network analysis has been successfully carried out.

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