Extreme Learning Machine Neural Networks for Multi-Agent System in Power Generation

Abstract

Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. The hidden neurons are optional in neuron alike whereas the weights are the criteria required to study the linking among the output layer as well as hidden layers. On the other hand, the ensemble model to integrate every independent prediction of several ELMs to produce a final output. This particular approach was included in a Multi-Agent System (MAS). By hybrid those two approached, a novel extreme learning machine based multi-agent systems (ELM-MAS) for handling classification problems is presented in this paper. It contains two layers of ELMs, i.e., individual agent layer and parent agent layer. Several activation functions using benchmark datasets and real-world applications, i.e., satellite image, image segmentation, fault diagnosis in power generation (including circulating water systems as well as GAST governor) were used to test the ELM-MAS developed. Our experimental results suggest that ELM-MAS is capable of achieving good accuracy rates relative to others approaches.

A State-of-the-Art Survey on Various Domains of Multi-Agent Systems and Machine Learning

“…Even though Yu [14][15][16][17][18] as coauthor is the main contributor in the area. He accounts for five articles, followed by Li [19][20][21][22], Mohammed [23][24][25][26], Wong [27][28][29][30], Yap [27][28][29][30], and Yaw [27][28][29][30] with four articles each one. Figure 5 presents the coauthor collaboration network from articles and chapter book published during the 2017-2022 period time, on multi-agent systems and machine learning applications, based on full counting.…”

Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review

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