最后将粒子群算法优化神经网络引入故障模式识别中。
Finally, the paper used particle swarm optimized neural network into fault pattern recognition.
说明：基于粒子群的神经网络优化算法的应用，在土壤水分特征曲线中的应用。
Neural network based on particle swarm optimization algorithm applied in the soil moisture characteristic curve application.
提出的自适应粒子群优化算法，用于优化多层前馈神经网络的拓扑结构，提高了神经网络的学习质量和速度。
The structure of multi-layer feedback forward neural network is optimized by improved PSO. Learning quality and training speed of the neural network are improved.
模糊神经网络的学习算法采用的是快速的粒子群优化算法。
A fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
在此基础上，提出基于改进粒子群算法优化的磁轴承神经网络PID控制方案。
On this basis, improved particle swarm optimization based on the magnetic bearing neural network PID control scheme is proposed.
二是利用基于粒子群算法(PSO)优化的BP神经网络进行异步电机故障诊断。
Another is to examine faults of asynchronous Motors in terms of BP neural network based on Particle Swarm Optimization (PSO).
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
利用改进粒子群算法替代BP算法优化神经网络的权值系数。
Alternative use of improved particle swarm optimization neural network BP algorithm weight value.
并把改进的粒子群优化算法和BP神经网络相结合，应用于变压器故障检测中。
Otherwise, the authors combined MDPSO and BP neural network and applied it to the diagnosis of power transformer.
采用粒子群算法对镗孔加工尺寸误差人工神经网络预测模型进行优化。
This paper presented particle swarm optimization (PSO) technique to train multi layer artificial neural network for predicting model of diameter errors of boring processes.
采用粒子群算法对镗孔加工尺寸误差人工神经网络预测模型进行优化。
This paper presented particle swarm optimization (PSO) technique to train multi layer artificial neural network for predicting model of diameter errors of boring processes.
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