摘要
极限学习机在实际应用中具有学习速度快、训练误差小的优点,但其稳定性与泛化能力却较差。针对其缺点,将自适应差分进化算法引入极限学习机对其改进,利用自适应差分进化算法的全局寻优能力,求取训练误差较小时极限学习机的输入权值矩阵以及隐含层偏置矩阵,从而优化极限学习机。将优化后的极限学习机应用于球磨机料位测量,实验结果表明,优化后的极限学习机与传统极限学习机相比具有较高的测量精度和较好的稳定性。
The advantages of extreme learning machine has strong learning capacity and smaller training deviation. To further improve the reliability and decreasing the test deviation of extreme learning machine,self- adaptive differential algorithm was introduced to extreme learning machine. Then smaller test deviation of sample sets was acquired in this way. Lastly,optimized extreme learning machine was applied to measure the ball mill material level. The experiment result shows that the test deviation and training deviation of this method are largely smaller than that of extreme learning machine. At the same time,learning capacity and generalization performance of this method are also better than that of original extreme learning machine.
出处
《仪表技术与传感器》
CSCD
北大核心
2015年第6期143-145,共3页
Instrument Technique and Sensor
基金
国家自然科学基金项目(60975032)
山西省自然科学基金项目(2011011012-2)
关键词
自适应差分进化算法
极限学习机
测试误差
球磨机料位测量
self-adaptive differential algorithm
extreme learning machine
test training deviation
ball mill material level
