An adaptive training algorithm for back-propagation neural networks

IEEE Transactions on Systems Man and Cybernetics ◽

10.1109/21.364864 ◽

1995 ◽

Vol 25 (3) ◽

pp. 512-514 ◽

Author(s):

Hsi-Chin Hsin ◽

Ching-Chung Li ◽

Mingui Sun ◽

R.J. Sclabassi

Keyword(s):

Neural Networks ◽

Back Propagation ◽

Training Algorithm ◽

Adaptive Training ◽

Back Propagation Neural Networks

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An adaptive training algorithm for back-propagation neural networks

[Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics ◽

10.1109/icsmc.1992.271653 ◽

2003 ◽

Author(s):

H.-C. Hsin ◽

C.-C. Li ◽

M. Sun ◽

R.J. Sclabassi

Keyword(s):

Neural Networks ◽

Back Propagation ◽

Training Algorithm ◽

Adaptive Training ◽

Back Propagation Neural Networks

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Synchronization control for completely unknown chaotic systems via nested back-propagation neural networks

2021 13th International Conference on Advanced Computational Intelligence (ICACI) ◽

10.1109/icaci52617.2021.9435885 ◽

2021 ◽

Author(s):

Xiaoling Song ◽

Zilin Gao ◽

Xitao Zou ◽

Liyuan Qi ◽

Yuan Luo

Keyword(s):

Neural Networks ◽

Chaotic Systems ◽

Back Propagation ◽

Synchronization Control ◽

Back Propagation Neural Networks ◽

Unknown Chaotic Systems

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Application of Adaptive Back-Propagation Neural Networks for Parkinson’s Disease Prediction

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI) ◽

10.1109/icdabi51230.2020.9325709 ◽

2020 ◽

Author(s):

Jawad Rasheed ◽

Alaa Ali Hameed ◽

Naim Ajlouni ◽

Akhtar Jamil ◽

Adem Ozyavas ◽

...

Keyword(s):

Neural Networks ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Back Propagation ◽

Disease Prediction ◽

Back Propagation Neural Networks

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General Approach for Training Back-Propagation Neural Networks in Vibration Control of Multidegree-of-Freedom Structures

Journal of Computing in Civil Engineering ◽

10.1061/(asce)0887-3801(2006)20:4(247) ◽

2006 ◽

Vol 20 (4) ◽

pp. 247-260 ◽

Author(s):

Alok Madan

Keyword(s):

Neural Networks ◽

Vibration Control ◽

Back Propagation ◽

Back Propagation Neural Networks

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Bolt force prediction using simplified finite element model and back propagation neural networks

2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference ◽

10.1109/itnec.2016.7560415 ◽

2016 ◽

Author(s):

Yang Fei ◽

Gao Pengdong ◽

Lu Yongquan

Keyword(s):

Neural Networks ◽

Finite Element ◽

Finite Element Model ◽

Back Propagation ◽

Element Model ◽

Force Prediction ◽

Back Propagation Neural Networks ◽

Simplified Finite Element Model

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A Novel Approach for the Classification of Leukemia Using Artificial Bee Colony Optimization Technique and Back-Propagation Neural Networks

Proceedings of 2nd International Conference on Communication, Computing and Networking - Lecture Notes in Networks and Systems ◽

10.1007/978-981-13-1217-5_68 ◽

2018 ◽

pp. 685-694

Author(s):

Rudrani Sharma ◽

Rakesh Kumar

Keyword(s):

Neural Networks ◽

Artificial Bee Colony ◽

Optimization Technique ◽

Back Propagation ◽

Artificial Bee Colony Optimization ◽

Back Propagation Neural Networks ◽

Novel Approach ◽

Bee Colony Optimization

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A Reverse Approach in Optimizing Pass Parameters

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.113-116.1707 ◽

2010 ◽

Vol 113-116 ◽

pp. 1707-1711

Author(s):

Jian Hua Hu ◽

Yuan Hua Shuang

Keyword(s):

Neural Networks ◽

Finite Element Method ◽

Finite Element ◽

Learning Process ◽

Back Propagation ◽

Fem Simulation ◽

Good Convergence ◽

Back Propagation Neural Networks ◽

A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

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Signal processing in defect detection using back-propagation neural networks

NDT & E International ◽

10.1016/s0963-8695(02)00022-1 ◽

2002 ◽

Vol 35 (7) ◽

pp. 483-488 ◽

Author(s):

V Khandetsky ◽

I Antonyuk

Keyword(s):

Neural Networks ◽

Signal Processing ◽

Defect Detection ◽

Back Propagation ◽

Back Propagation Neural Networks

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Towards Fog Forecasting in Meteorology by Means of Back-Propagation Neural Networks with Weighted Least-Squares Training

Neural Nets WIRN VIETRI-96 - Perspectives in Neural Computing ◽

10.1007/978-1-4471-0951-8_30 ◽

1997 ◽

pp. 263-269 ◽

Author(s):

Antonello Pasini ◽

Sergio Potestà

Keyword(s):

Neural Networks ◽

Least Squares ◽

Weighted Least Squares ◽

Back Propagation ◽

Fog Forecasting ◽

Back Propagation Neural Networks

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Heart Block Recognition Using Image Processing and Back Propagation Neural Networks

Soft Computing Systems - Communications in Computer and Information Science ◽

10.1007/978-981-13-1936-5_23 ◽

2018 ◽

pp. 200-210

Author(s):

P. Asha ◽

B. Sravani ◽

P. SatyaPriya

Keyword(s):

Neural Networks ◽

Image Processing ◽

Heart Block ◽

Back Propagation ◽

Back Propagation Neural Networks

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