Sleep Sensor Temperature Prediction Based on Neural Network Training Algorithm with Rational Spline Weight Functions

In wireless sensor network, it is necessary to make effective prediction of sensor node’s data during its sleep period. In this paper a model of rational cubic spline weight function (SWF) neural network with linear denominator was established for sensor node’s temperature prediction. This kind of rational spline function is denoted by 3/1 rational splines. Then we trained and tested the network, the simulation results showed that, compared to the traditional BP neural network, the training speed is higher and the error is smaller. Therefore the prediction model can effectively predict the sensor’s temperature.

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A Neural Network Training Algorithm Based on Collinear Scaling Quasi-Newton Method

2011 Fourth International Conference on Information and Computing ◽

10.1109/icic.2011.22 ◽

2011 ◽

Author(s):

Shijie Ye ◽

Jianliang Li ◽

Jun Xu

Keyword(s):

Neural Network ◽

Newton Method ◽

Neural Network Training ◽

Training Algorithm ◽

Network Training ◽

Quasi Newton

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Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River, India

International Journal of Sediment Research ◽

10.1016/j.ijsrc.2018.10.010 ◽

2019 ◽

Vol 34 (2) ◽

pp. 125-135 ◽

Cited By ~ 7

Author(s):

Nibedita Bisoyi ◽

Harish Gupta ◽

Narayan Prasad Padhy ◽

Govind Joseph Chakrapani

Keyword(s):

Neural Network ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Neural Network Training ◽

Sediment Discharge ◽

Training Algorithm ◽

Network Training ◽

Narmada River

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Machine-learning in astronomy

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314013672 ◽

2014 ◽

Vol 10 (S306) ◽

pp. 279-287 ◽

Cited By ~ 2

Author(s):

Michael Hobson ◽

Philip Graff ◽

Farhan Feroz ◽

Anthony Lasenby

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Gamma Ray ◽

Neural Network Training ◽

Training Algorithm ◽

Data Description ◽

Astronomical Data ◽

Machine Learning Methods ◽

Network Training

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

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Research on the Optimized Algorithms on Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.2175 ◽

2012 ◽

Vol 605-607 ◽

pp. 2175-2178

Author(s):

Xiao Qin Wu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Simulated Annealing Algorithm ◽

Fitness Function ◽

Strong Stability ◽

Training Algorithm ◽

Network Training ◽

The Neural Network ◽

Annealing Algorithm ◽

Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

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