scholarly journals An ELM-based single input rule module and its application in power generation

2020 ◽  
Vol 11 (1) ◽  
pp. 359
Author(s):  
Chong Tak Yaw ◽  
Shen Young Wong ◽  
Keem Sian Yap
Keyword(s):  
Power Generation ◽  
Extreme Learning Machine ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Benchmark Datasets ◽  
Single Input ◽  
Learning Machine ◽  
Hidden Neurons

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. In traditional fuzzy inference method which was the "if-then" rules, all the input and output objects were assigned to antecedent and consequent component respectively. However, a major dilemma was that the fuzzy rules' number kept increasing until the system and arrangement of the rules became complicated. Therefore, the single input rule modules connected type fuzzy inference (SIRM) method where consociated the output of the fuzzy rules modules significantly. In this paper, we put forward a novel single input rule modules based on extreme learning machine (denoted as SIRM-ELM) for solving data regression problems. In this hybrid model, the concept of SIRM is applied as hidden neurons of ELM and each of them represents a single input fuzzy rules. Hence, the number of fuzzy rule and the number of hidden neuron of ELM are the same. The effectiveness of proposed SIRM-ELM model is verified using sigmoid activation functions based on several benchmark datasets and a NOx emission of power generation plant.  Experimental results illustrate that our proposed SIRM-ELM model is capable of achieving small root mean square error, i.e., 0.027448 for prediction of NO<sub>x</sub> emission.

scholarly journals Extreme Learning Machine with Multi-Agent System for Regression

2019 ◽  
Vol 9 (2) ◽  
pp. 4149-4153
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Individual Agent ◽  
Learning Speed ◽  
Benchmark Datasets ◽  
Multi Agent ◽  
Learning Machine ◽  
Hidden Neurons

From the point of learning speed as well as generalization, Extreme Learning Machine(ELM) is widely known as an effective learning algorithm than the conventional learning methods. Basically, hidden neurons are not required in neuron alike, instead, weight is the parameter that would need to learn about the link in between output and hidden layers. The creation of an output is to integrate each independent of several ELMs. The precise approach is included in a Multi-Agent System. The novelty of ELM-MAS (extreme learning machine based multi-agent system) is put forward in the paper for solving data regression problems. The ELMs consist of two layers which are the parent agent layer and individual agent layer. The effectiveness of the ELM-MAS model is proved with some activation functions employing benchmark datasets (abalone, strike and space-ga) and real world application (Nox emission). The outcomes indicate that the proposed model is capable to attain improved results than other approaches.

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

2018 ◽  
Vol 7 (4.35) ◽  
pp. 347
Author(s):  
Chong Tak Yaw ◽  
Shen Yuong Wong ◽  
Keem Siah Yap ◽  
Chin Hooi Tan
Keyword(s):  
Power Generation ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Satellite Image ◽  
Multi Agent System ◽  
Classification Problems ◽  
Agent System ◽  
Individual Agent ◽  
Multi Agent ◽  
Learning Machine

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.

scholarly journals A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Imen Jammoussi ◽  
Mounir Ben Nasr
Keyword(s):  
Extreme Learning Machine ◽  
Inverse Method ◽  
Learning Algorithm ◽  
Self Organizing Map ◽  
Random Parameters ◽  
Fast Learning ◽  
Self Organized ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are considered to demonstrate the efficiency of the proposed approach compared with original extreme learning machine, Tikhonov regularization optimally pruned extreme learning machine, and backpropagation algorithms. The results show that the proposed method is fast and produces better accuracy and generalization performances.

A Reinforcement Learning Scheme of Fuzzy Rules with Reduced Conditions

2000 ◽  
Vol 4 (2) ◽  
pp. 146-151 ◽  
Author(s):  
Hiroshi Kawakami ◽  
◽  
Osamu Katai ◽  
Tadataka Konishi ◽  
Keyword(s):  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Q Value ◽  
Huge Number ◽  
Q Learning ◽  
Control Rules ◽  
A Value ◽  
Rule System

This paper proposes a new method of Q-learning for the case where the states (conditions) and actions of systems are assumed to be continuous. The components of Q-tables are interpolated by fuzzy inference. The initial set of fuzzy rules is made of all combinations of conditions and actions relevant to the problem. Each rule is then associated with a value by which the Q-values of condition/action pairs are estimated. The values are revised by the Q-learning algorithm so as to make the fuzzy rule system effective. Although this framework may require a huge number of the initial fuzzy rules, we will show that considerable reduction of the number can be done by adopting what we call Condition Reduced Fuzzy Rules (CRFR). The antecedent part of CRFR consists of all actions and the selected conditions, and its consequent is set to be its Q-value. Finally, experimental results show that controllers with CRFRs perform equally well to the system with the most detailed fuzzy control rules, while the total number of parameters that have to be revised through the whole learning process is considerably reduced, and the number of the revised parameters at each step of learning increased.

Two-Motor Synchronous Decoupling Control Based on Improved Incremental Regularized Extreme Learning Machine

2013 ◽  
Vol 765-767 ◽  
pp. 1854-1857
Author(s):  
Feng Wang ◽  
Jin Lin Ding ◽  
Hong Sun
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Decoupling Control ◽  
Loop Control ◽  
Fast Learning ◽  
Optimal Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons

Neural network generalized inverse (NNGI) can realize two-motor synchronous decoupling control, but traditional neural network (NN) exists many shortcomings, Regular extreme learning machine (RELM) has fast learning and good generalization ability, which is an ideal approach to approximate inverse system. But it is difficult to accurately give the reasonable number of hidden neurons. Improved incremental RELM(IIRELM) is prospected on the basis of analyzing RELM learning algorithm, which can automatically determine optimal network structure through gradually adding new hidden-layer neurons, and prediction model based on IIRELM is applied in two-motor closed-loop control based on NNGI, the decoupling control between velocity and tension is realized. The experimental results proved that the system has excellent performance.

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

Prediction of Software Defect Using Ensemble Learning Based Improved Sparrow Search Algorithm To Optimize Extreme Learning Machine

2021 ◽  
Author(s):  
Yu Tang ◽  
Qi Dai ◽  
Mengyuan Yang ◽  
Lifang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Prediction Ability ◽  
Software Defect ◽  
Ensemble Learning Algorithm ◽  
Learning Machine

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

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