改进鲸鱼算法优化核极限学习机在水质光谱分析中的应用

林春伟; 郭永洪; 何金龙

doi:doi:10.16818/j.issn1001-5868.2019.01.023

半导体光电, 2019, 40 (1): 112, 网络出版: 2019-03-25

改进鲸鱼算法优化核极限学习机在水质光谱分析中的应用

Application of Kernel Extreme Learning Machine Optimized by Improved Whale Algorithm in Water Quality Spectrum Analysis

论文大纲

林春伟郭永洪 ^*何金龙

作者单位

中国计量大学机电工程学院, 杭州 310018

摘要

为提高水质光谱分析模型的学习速度与预测精度, 采用核极限学习机对水质光谱进行建模, 并提出一种具有动态惯性权重的改进鲸鱼优化算法对模型进行参数优化。由于极限学习机的输入权值矩阵和偏置是随机生成的, 故引入核方法以减小其输出权值矩阵的波动; 将鲸鱼优化算法中的惯性权重在非线性递减的基础上引入随机因子, 通过动态调整惯性权重以平衡算法的全局搜索能力与局部开发能力。与传统优化模型进行了对比实验, 实验结果表明: 基于该方法所建模型具有更高的预测精度, 而在相同的学习迭代次数下, 核极限学习机的运行时间相对于传统算法约下降50%, 且改进鲸鱼优化算法能够以更快的收敛速度使模型达到全局最优。

Abstract

In order to improve the learning speed and prediction accuracy of water quality spectrum analysis model, kernel extreme learning machine was used to model the water quality spectrum, and an improved whale optimization algorithm with dynamic inertia weight was proposed to optimize the model parameters. Since the input weight matrix and bias of the extreme learning machine are generated randomly, the kernel method was introduced to reduce the fluctuation of the output weight matrix. The inertia weight of whale optimization algorithm was introduced into the random factor based on nonlinear decreasing, and the dynamic adjustment of inertia weight was used to balance the global search capability and the local development ability. Compared with the traditional optimization model, the experimental results show that the model based on this method has higher prediction accuracy, and under the same number of learning iterations, the running time of the kernel extreme learning machine is about 50% lower than traditional algorithm, and the improved whale optimization algorithm enables the model to achieve global optimum at a faster convergence rate.

参考文献

[1] 侯迪波, 张坚, 陈泠, 等. 基于紫外-可见光光谱的水质分析方法研究进展与应用［J］. 光谱学与光谱分析, 2013, 33(7): 1839-1844.

Hou Dibo, Zhang Jian, Chen Ling, et al. Water quality analysis by UV-vis spectroscopy: A review of methodology and application［J］. Spectroscopy and Spectral Anal., 2013, 33(7): 1839-1844.

[2] Huo Aidi, Zhang Jia, Qiao Changlu, et al. Multispectral remote sensing inversion for city landscape water eutrophication based on genetic algorithm-support vector machine［J］. Water Quality Research J. of Canada, 2014, 49(3): 285-293.

[3] Wang Xuan, Lv Jiake, Xie Deti. A hybrid approach of support vector machine with particle swarm optimization for water quality prediction［C］// Inter. Conf. on Computer Science and Education, 2010, 58(3): 1158-1163.

[4] Huang Guangbin. An insight into extreme learning machines: Random neurons, random features and kernels［J］. Cognitive Computation, 2014, 6(3): 376-390.

[5] Vahid K, Faramarz D A, Saeed Y, et al. Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods［J］. Geoderma, 2018, 318: 29-41.

[6] 周美灵, 郑德智, 娄格, 等. 基于改进极限学习机的光谱定量建模方法［J］. 北京航空航天大学学报, 2017, 43(6): 1208-1215.

Zhou Meiling, Zheng Dezhi, Lou Ge, et al. Quantitative spectral modeling method based on improved extreme learning machine［J］. J. of Beijing University of Aeronautics and Astronautics, 2017, 43(6): 1208-1215.

[7] Das P P, Bisoi R, Dash P K. Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis［J］. Expert Systems with Appl., 2018, 96: 427-499.

[8] Wang Mingjing, Chen Huiling, Yang Bo, et al. Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses［J］. Neurocomputing, 2017, 267: 69-84.

[9] Mirjalili S, Lewis A. The whale optimization algorithm［J］. Advances in Engin. Software, 2016, 95: 51-67.

[10] 龙文, 蔡绍洪, 焦建军, 等. 求解大规模优化问题的改进鲸鱼优化算法［J］. 系统工程理论与实践, 2017, 37(11): 2983-2994.

Long Wen, Cai Shaohong, Jiao Jianjun, et al. Improved whale optimization algorithm for large scale optimization problems［J］. Systems Engin. Theory and Practice, 2017, 37(11): 2983-2994.

[11] Diego O, Mohamed A E A, Aboul E H. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm［J］. Appl. Energy, 2017, 200: 141-154.

[12] Lu Huijuan, Du Bangjun. A kernel extreme learning machine algorithm based on improved particle swarm optimization［J］. Memetic Computing, 2016, 9(2): 1-8.

[13] 董红斌, 李冬锦, 张小平. 一种动态调整惯性权重的粒子群优化算法［J］. 计算机科学, 2018, 45(2): 98-102.

Dong Hongbin, Li Dongjin, Zhang Xiaoping. Particle swarm optimization algorithm with dynamically adjusting inertia weight［J］. Computer Science, 2018, 45(2): 98-102.

[14] 周欢, 李煜. 具有动态惯性权重的布谷鸟搜索算法［J］. 智能系统学报, 2015, 10(4): 645-651.

Zhou Huan, Li Yu. Cuckoo search algorithm with dynamic inertia weight［J］. CAAI Trans. on Intelligent Systems, 2015, 10(4): 645-651.

[15] Li Xuejun, Xu Jia, Zhu Erzhou, et al. A novel computation method for adaptive inertia weight of task scheduling algorithm［J］. J. of Computer Research and Development, 2016, 53(9): 1990-1999.

林春伟, 郭永洪, 何金龙. 改进鲸鱼算法优化核极限学习机在水质光谱分析中的应用[J]. 半导体光电, 2019, 40(1): 112. LIN Chunwei, GUO Yonghong, HE Jinlong. Application of Kernel Extreme Learning Machine Optimized by Improved Whale Algorithm in Water Quality Spectrum Analysis[J]. Semiconductor Optoelectronics, 2019, 40(1): 112.

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