Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System
Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System
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DOI：
10.1109/access.2020.3012983
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
C. T. Yaw;K. S. Yap;S. Wong;H. Yap;J. Paw
中科院分区：
文献类型：
--
作者：
C. T. Yaw;K. S. Yap;S. Wong;H. Yap;J. Paw
极限学习机除了解析地确定输出权值外，还通过随机选择隐含神经元来改进权值调整的迭代过程。本文对基本ELM神经网络进行了改进，简化了网络结构，以达到回归性能。其次，为了解决模式分类问题，提出了一种将ELM神经网络和MAS模型相结合的混合系统。在此基础上，结合ELM神经网络，提出了一种新的信任度量方法，设计了MAS模型。首先，设计了单输入规则模块混合ELM(SIRM-ELM)。只有一个输入与规则相连，其中规则是ELM的隐含神经元，每个规则代表一个输入模糊规则。结果表明，SIRM-ELM模型优于支持向量机和传统ELM模型。其次，设计了一种基于极限学习机的多智能体系统(ELM-MAS)，以提高ELM的性能。它的第一层由至少一个ELM组成，其中ELM作为单独的代理，而另一层由单个ELM作为父代理组成。最后，将认证强度信念(CBS)方法应用于ELM神经网络，形成ELM-MAS-CBS，以个体智能体的声誉和强度作为信任度量。与ELM代理相关的强元素的集合形成了信任管理，该信任管理允许使用CBS方法提高MAS的性能。对所开发的模型在发电系统中的应用进行了评估。两种模型对循环水系统的测试准确率均可与其他算法相媲美。简而言之，所开发的模型已经使用基准数据集进行了验证，并应用于发电，结果令人满意。
Extreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM’s capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory.