PMSM Control Research Based on Particle Swarm Optimization BP Neural Network

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

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Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.511-512.941 ◽

2014 ◽

Vol 511-512 ◽

pp. 941-944 ◽

Cited By ~ 1

Author(s):

Hong Li Bian

Keyword(s):

Neural Network ◽

Time Series ◽

Particle Swarm Optimization ◽

Bp Neural Network ◽

Time Series Prediction ◽

Particle Swarm ◽

Chaotic Time Series ◽

Learning Ability ◽

Swarm Optimization ◽

Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

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Application BP Neural Network in the Speaker Recognition Based on Chaos Particle Swarm Optimization Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2805 ◽

2013 ◽

Vol 765-767 ◽

pp. 2805-2808

Author(s):

Guo Wen Wang ◽

Shi Xin Luo ◽

Li He ◽

Gang Yin

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Speaker Recognition ◽

Particle Swarm Optimization Algorithm ◽

Recognition Rate ◽

Particle Swarm ◽

Bp Network ◽

Swarm Optimization

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.

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