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计算机系统应用英文版:2020,29(2):163-168
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基于模拟退火算法的改进极限学习机
(中国科学技术大学 管理学院, 合肥 230026)
Improved Extreme Learning Machine Based on Simulated Annealing Algorithm
(School of Management, University of Science and Technology of China, Hefei 230026, China)
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Received:June 25, 2019    Revised:July 23, 2019
中文摘要: 传统的极限学习机作为一种有监督的学习模型,任意对隐藏层神经元的输入权值和偏置进行赋值,通过计算隐藏层神经元的输出权值完成学习过程.针对传统的极限学习机在数据分析预测研究中存在预测精度不足的问题,提出一种基于模拟退火算法改进的极限学习机.首先,利用传统的极限学习机对训练集进行学习,得到隐藏层神经元的输出权值,选取预测结果评价标准.然后利用模拟退火算法,将传统的极限学习机隐藏层输入权值和偏置视为初始解,预测结果评价标准视为目标函数,通过模拟退火的降温过程,找到最优解即学习过程中预测误差最小的极限学习机的隐藏层神经元输入权值和偏置,最后通过传统的极限学习机计算得到隐藏层输出权值.实验选取鸢尾花分类数据和波士顿房价预测数据进行分析.实验发现与传统的极限学习机相比,基于模拟退火改进的极限学习机在分类和回归性能上都更优.
Abstract:As a supervised learning model, traditional extreme learning machine assigns input weights and bias of nodes of hidden layer arbitrarily, and completes learning process by calculating output weights of nodes of hidden layer. Aiming at the problem that traditional extreme learning machine does not work well in prediction research, an improved extreme learning machine model based on simulated annealing algorithm was proposed. Firstly, traditional extreme learning machine method was used to learn the training set, and output weight of hidden layer is obtained. The evaluation standard of prediction result was selected to assess prediction result. Then, using the simulated annealing algorithm, input weights and bias of hidden layer of traditional extreme learning machine were regarded as the initial solution, and the evaluation standard was regarded as the objective function. The optimal solution was found in cooling process that was input weights and bias of hidden layer of extreme learning machine with the smallest prediction error. Iris classification data and Boston house price forecast data were used to conduct experiments. The experiment finds that compared with traditional extreme learning machine, extreme learning machine based on simulated annealing is better on classification and regression.
文章编号:7276     中图分类号:    文献标志码:
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引用文本:
吴雨.基于模拟退火算法的改进极限学习机.计算机系统应用,2020,29(2):163-168
WU Yu.Improved Extreme Learning Machine Based on Simulated Annealing Algorithm.COMPUTER SYSTEMS APPLICATIONS,2020,29(2):163-168