Evaluation of side orifices shape factor using the novel approach self-adaptive extreme learning machine

2019 ◽  
Vol 5 (3) ◽  
pp. 925-935
Author(s):  
Ali Reza Mahmodian ◽  
Ahmad Rajabi ◽  
Mohammad Ali Izadbakhsh ◽  
Saeid Shabanlou
Keyword(s):  
Extreme Learning Machine ◽  
Shape Factor ◽  
The Novel ◽  
Novel Approach ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

2020 ◽  
Vol 2 (11) ◽  
Author(s):  
Clement Kweku Arthur ◽  
Victor Amoako Temeng ◽  
Yao Yevenyo Ziggah
Keyword(s):  
Extreme Learning Machine ◽  
Ground Vibration ◽  
Vibration Prediction ◽  
Novel Approach ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals A Novel Mating Approach for Genetic Algorithms

2013 ◽  
Vol 21 (2) ◽  
pp. 197-229 ◽  
Author(s):  
Severino F. Galán ◽  
Ole J. Mengshoel ◽  
Rafael Pinter
Keyword(s):  
Genetic Algorithms ◽  
Random Mating ◽  
Adaptive Strategy ◽  
Mating Strategies ◽  
Function Optimization ◽  
The Novel ◽  
Independent Manner ◽  
Novel Approach ◽  
Domain Independent ◽  
Self Adaptive

Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering

2016 ◽  
Vol 8 (4) ◽  
pp. 720-728 ◽  
Author(s):  
Li Xu ◽  
Shifei Ding ◽  
Xinzheng Xu ◽  
Nan Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Affinity Propagation ◽  
Affinity Propagation Clustering ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jingyu Zhou ◽  
Shulin Tian ◽  
Chenglin Yang ◽  
Xuelong Ren
Keyword(s):  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Test Generation ◽  
Cost Effective ◽  
Fault Classification ◽  
The Novel ◽  
Analog Device ◽  
Generation Algorithm ◽  
New Process ◽  
Learning Machine

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.

scholarly journals Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection

2017 ◽  
Vol 4 (2) ◽  
pp. 024507 ◽  
Author(s):  
Kadayanallur Mahadevan Prabusankarlal ◽  
Palanisamy Thirumoorthy ◽  
Radhakrishnan Manavalan
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Adaptive Differential Evolution ◽  
Learning Machine ◽  
Self Adaptive
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