A new inconsistent context fusion algorithm based on BP neural network and modified DST

Optimization problem is the problem which can be often encountered mostly in industrial design, and the key of optimization is to find the global optimum and higher constriction speed. This paper proposes a PSO algorithm based on BP neural network by neural network trains and selects individual extreme best randomly, to make the particle follow the optimal particle in the solution space search, and obtain the optimum extreme best in the whole situation. Through the application of the simulation experiment on image segmentation showed that the algorithm is suitable in dealing with multiple types function and constraint, with fast convergence speed, and easy combination with traditional optimization methods, thus improving its own limitations, and solving problems more efficiently.

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Research on fusion algorithm with BP neural network and rough set

2011 6th IEEE Conference on Industrial Electronics and Applications ◽

10.1109/iciea.2011.5975561 ◽

2011 ◽

Author(s):

Li Wei ◽

He Peng-ju ◽

Zhang Peng ◽

Chen Ming ◽

Jiang Li-jun

Keyword(s):

Neural Network ◽

Rough Set ◽

Bp Neural Network ◽

Fusion Algorithm

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A Grain Output Combination Forecast Model Modified by Data Fusion Algorithm

Journal of Intelligent Systems ◽

10.1515/jisys-2016-0072 ◽

2018 ◽

Vol 27 (2) ◽

pp. 303-315

Author(s):

Xiang Wan ◽

Bing-Xiang Liu ◽

Xing Xu

Keyword(s):

Neural Network ◽

Data Fusion ◽

Bp Neural Network ◽

Particle Swarm Optimization Algorithm ◽

Back Propagation ◽

Forecast Model ◽

Fusion Algorithm ◽

Combination Model ◽

Combination Forecast ◽

Use Of Data

AbstractTo deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of – particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm – to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.

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Study on Data Security Technology of Neural Network System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.701-702.1041 ◽

2014 ◽

Vol 701-702 ◽

pp. 1041-1044

Author(s):

Yan Wei Hong

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Security Technology ◽

Fusion Algorithm ◽

Basic Principles ◽

Network Algorithm ◽

The Neural Network ◽

Neural Network System ◽

Neural Network Algorithm ◽

And Training

This paper analyzes the neural network algorithm model, introduces the basic principles and training process of BP neural network algorithm, analyzes the BP neural network weights adjustment processand the method of determining the number of nodes in each layer; in improved protocol algorithm basis LEACH-E, combined with the BP neural network algorithm, we propose a new data fusion algorithm BPDFA to reduce energy consumption to attain the network lifetime goal.

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