Study on Rock Bolt Support of Roadway of Coal Mine Using Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 3799-3802
Author(s):  
Feng Shan Han ◽  
Xin Li Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Mine ◽  
Engineering Application ◽  
Rock Bolt ◽  
Learning System ◽  
Artificial Neural ◽  
Complex Relationships ◽  
Rock Bolt Support ◽  
Bolt Support

The artificial neural network has been widely used in various field of science and engineering. The artificial neural network has marvelous ability to gain knowledge. In this paper, according to principle of artificial neural network , Model of artificial neural network of rock bolt support of roadway of coal mine has been constructed,Learning system of BP artificial neural network has been trained,it is shown by engineering application that artificial neural network can handle imperfect or incomplete data and it can capture nonlinear and complex relationships among variables of a system. the artificial neural network is emerging as a powerful tool for modeling with the complex system. Method and parameters of rock bolt support of roadway of coal mine can be predicated accurately using artificial neural network, that is of significance and valuable to those subjects of investigation and design of mining engineering

Literature Survey for Applications of Artificial Neural Networks

2022 ◽  
pp. 669-682
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Quick Acquisition of a Control Algorithm for Small Flying Robots by a Learning System Combining an Artificial Neural Network and an Immunity Algorithm

2007 ◽  
Author(s):  
Tohru Watanabe ◽  
Hideki Wakamatsu ◽  
Hidehiko Nishiwaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Algorithm ◽  
Back Propagation ◽  
Learning System ◽  
Local Optimum ◽  
State Variables ◽  
Control Loops ◽  
Artificial Neural ◽  
Flying Robots

Flying robots: small computer controlled airplanes are useful for safe and economical investigation with aerial photography. We have developed such planes using DSP boards equipped with sensors and many PID control loops. However, the tuning of their control parameters is difficult because of many state variables, interference between control loops, and the nonlinear characteristics of aviation dynamics. In this paper, the self-organization of their control algorithm is proposed. An artificial neural network is used as the controller. The control algorithm is organized by back propagation type learning to decrease the difference between the desired state variables and the estimated ones. A problem of back propagation-type learning is apt to appear in local optimum solutions. Therefore, many sets of initial values of input gains at the synapses of neurons are prepared similar to DNA codes in immune bodies for immunity process simulation. When a local optimum solution of the neural network is found, the DNA code of learned gains is memorized at the T cell of the immunity process. Cells near these having the memorized DNA are eliminated, and new cells far from them are added to the process in searching for better solutions. It is verified by computer simulation that a better control algorithm is automatically organized for small flying robots having complicated dynamics.

Literature Survey for Applications of Artificial Neural Networks

2021 ◽  
pp. 1-17
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Proposition of Machine Learning Driven Personalized Marketing Approach for E-Commerce

2019 ◽  
Vol 16 (8) ◽  
pp. 3532-3537 ◽  
Author(s):  
Kok Sheng Tan ◽  
Preethi Subramanian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Artificial Neural Network ◽  
Customer Segmentation ◽  
Customer Preferences ◽  
Artificial Neural ◽  
Complex Relationships ◽  
Buying Behaviour ◽  
Marketing Approach

The ubiquity of digital devices and Internet has formed a constantly connected online environment which led to the extensive adoption of e-commerce. However, the active participation of growing number of stakeholders intensifies the highly competitive landscape of the dynamic e-commerce market and the scarcity of trust in e-commerce business impede the generation of consistent sales growth. The obstruction necessitates the implementation of innovative marketing strategies to enhance the relationships with customers to develop customer loyalty. Therefore, a machine learning driven personalized marketing approach is proposed to facilitate the implementation of personalized marketing in which there are 2 significant sequential elements namely, the development of personalized marketing contents and delivery of the contents to prospective customers. Cluster analysis is employed to perform customer segmentation to discover customer segments due to the capability of the analysis to identify similarities in customer preferences in which the discovered customer segments are used to construct personalized marketing contents. In addition, artificial neural network is employed to predict prospective customers due to the capability of artificial neural network to comprehend complex relationships between customer demographics and buying behaviour in which the prediction facilitates the delivery of the constructed personalized marketing contents to potential repeat customer to optimize the marketing initiative. The combination of cluster analysis and artificial neural network empowers the construction of an efficacious marketing pipeline which enhances the competency of e-commerce businesses.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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