scholarly journals Swarm Optimization Improved BP Algorithm for Microchannel Resistance Factor

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52749-52758
Author(s):  
Teng Shen ◽  
Jiaqing Chang ◽  
Zhongwei Liang
Keyword(s):  
Resistance Factor ◽  
Bp Algorithm ◽  
Swarm Optimization ◽  
Improved Bp Algorithm

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

A New Kind of Optimization Method for Gear Design

2012 ◽  
Vol 190-191 ◽  
pp. 7-10 ◽  
Author(s):  
Yu Guo Wu
Keyword(s):  
Simulated Annealing Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Bp Algorithm ◽  
Mean Square ◽  
Test Results ◽  
Swarm Optimization ◽  
Design Efficiency ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, based on PSO algorithm and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

Character Image Recognition Based on the Improved BP Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 2655-2658 ◽  
Author(s):  
Hong Zhao
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Simulation Results ◽  
Improved Bp Algorithm

This paper raises a kind of improved BP algorithm in order to compensate for some shortcomings which exist in traditional BP neural network. It has been applied to the recognition of character images. Computer simulation results demonstrate that it does bring about an ideal result.

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