NON-LINEAR DYNAMIC MODEL OF ROCK BURST BASED ON EVOLUTIONARY NEURAL NETWORK

The theory studies have showed that, rock burst is a kind of dynamic phenomenon of rock mass in mining, and is a kind of dynamic disaster from mining. The time series of magnitude is a very important exterior behavior of rock burst. The previous studies show that, to model this complicated non-linear time series, the neural network is a very good method. To overcome the shortcomings of traditional neural network, a new kind of evolutionary neural network based on immunized evolutionary programming proposed by author is proposed here. At last, the proposed evolutionary neural network model is verified by a real magnitude series of rock burst. And the result is compared with other method, such as grey system method. The results have showed that, evolutionary neural network model not only has high approaching precision, but also has high predicting precision, and is a good method to construct the non-linear model of rock burst. And this method can be used in a large number of engineering examples.

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Particle Swarm optimization based Neural Network Model for Chaotic Time Series Forecasting

2020 12th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci49185.2020.9177737 ◽

2020 ◽

Author(s):

Xin Li ◽

Weijie Ren ◽

Jingying Zhao ◽

Min Han

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Network Model ◽

Neural Network Model ◽

Particle Swarm ◽

Time Series Forecasting ◽

Chaotic Time Series ◽

Swarm Optimization

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An improved self-organizing incremental neural network model for short-term time-series load prediction

Applied Energy ◽

10.1016/j.apenergy.2021.116912 ◽

2021 ◽

Vol 292 ◽

pp. 116912

Author(s):

Rong Wang Ng ◽

Kasim Mumtaj Begam ◽

Rajprasad Kumar Rajkumar ◽

Yee Wan Wong ◽

Lee Wai Chong

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Load Prediction ◽

Short Term ◽

Self Organizing

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Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

Neural Processing Letters ◽

10.1007/s11063-014-9342-0 ◽

2014 ◽

Vol 41 (2) ◽

pp. 249-258 ◽

Cited By ~ 24

Author(s):

Erol Egrioglu ◽

Ufuk Yolcu ◽

Cagdas Hakan Aladag ◽

Eren Bas

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Neuron Model ◽

Linear Time ◽

Time Series Forecasting ◽

Non Linear ◽

Artificial Neural

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Grey RBF Neural Network Forecasting on LV under the View of Government Responsibility

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.700 ◽

2012 ◽

Vol 452-453 ◽

pp. 700-704

Author(s):

Feng Rong Zhang ◽

Annik Magerholm Fet ◽

Xin Wei Xiao

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Development Policy ◽

Rbf Neural Network ◽

Good Method ◽

Government Responsibility ◽

Long Period ◽

The Government

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.

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Wavelet neural network model for river flow time series

Proceedings of the Institution of Civil Engineers - Water Management ◽

10.1680/wama.10.00092 ◽

2012 ◽

Vol 165 (8) ◽

pp. 425-439 ◽

Cited By ~ 3

Author(s):

Budu Krishna ◽

Yellamelli Ramji Satyaji Rao ◽

Purna Chandra Nayak

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

River Flow ◽

Flow Time ◽

Wavelet Neural Network

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Effects of Data Pre-processing on the Prediction Accuracy of Artificial Neural Network Model in Hydrological Time Series

Urban Hydrology, Watershed Management and Socio-Economic Aspects - Water Science and Technology Library ◽

10.1007/978-3-319-40195-9_21 ◽

2016 ◽

pp. 265-275 ◽

Cited By ~ 3

Author(s):

Aniruddha Gopal Banhatti ◽

Paresh Chandra Deka

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Prediction Accuracy ◽

Artificial Neural Network Model ◽

Artificial Neural

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Towards Designing a Computationally Efficient Neural Network Model for Improved Financial Time Series Forecasting

Lecture Notes in Electrical Engineering - Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies ◽

10.1007/978-981-15-3125-5_58 ◽

2020 ◽

pp. 605-619

Author(s):

Sarat Chandra Nayak ◽

Koppula Vijaya Kumar ◽

B. Satyanarayana

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Financial Time Series ◽

Time Series Forecasting ◽

Computationally Efficient ◽

Financial Time

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Neural Network Model for Efficient portfolio Management and Time Series Forecasting

2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) ◽

10.1109/iciccs48265.2020.9121049 ◽

2020 ◽

Author(s):

Nikhitha Pai ◽

V Ilango

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Portfolio Management ◽

Time Series Forecasting

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Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model

Sustainable Computing Informatics and Systems ◽

10.1016/j.suscom.2020.100397 ◽

2020 ◽

Vol 27 ◽

pp. 100397

Author(s):

Li Lu ◽

Jijun Zhang ◽

Fan Yang ◽

Yongjian Zhang

Keyword(s):

Neural Network ◽

Time Series ◽

Total Factor Productivity ◽

Network Model ◽

Neural Network Model ◽

Factor Productivity ◽

Dea Model ◽

Petroleum Companies

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Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.583 ◽

2011 ◽

Vol 66-68 ◽

pp. 583-587 ◽

Cited By ~ 2

Author(s):

Jian Xiong Long

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Pulse Current ◽

The State ◽

State Of Charge ◽

Current Response ◽

Grey System ◽

Battery State Of Charge ◽

State Of Charge Estimation

In order to effectively achieve MH-Ni battery state of charge estimation, grey system neural network model is put forward to predict battery state of charge by using the parameters of battery pulse current response signal as input for grey system neural network. The state of charge is as the network output and the response parameters of the battery pulse current as the input. The results show that its prediction accuracy of the state of charge can be achieved to requirements of the electric vehicles in applications by this method to predict the state of charge.

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