Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

Abstract

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.

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

“… 2020 ), energy system (Yaw et al. 2020 ), mislabeled samples detection (Akusok et al. 2015 ), concept drift detection (Yang et al.…”

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 Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

“…To be precise, the focus of machine learning is based on neural networks [46,47,[50][51][52]54,56], support vector machine (SVM) [45,49], and extreme learning machine (ELM) [30], and have been widely used in switchgear system fault diagnosis. Literature studies state that by using extreme learning machine (ELM) the learning speed can be instantly quicker than conventional feed-forward neural network (FFNN) learning algorithms, while also achieving improved generalization performance [59][60][61][62][63][64][65]. It was able to come up with a universal approximation using random biases and input weights.…”

“…In addition, ELM tends to reach the smallest training error but also the smallest norm of considerable weights [66][67][68][69][70][71][72]. Therefore, the proposed learning algorithm undoubtedly tends to have a more precise, good generalization performance for FFNNs [63,64,67,70,72,73]. Intrinsically, ELM has been selected as an artificial intelligence tool for this article.…”