scholarly journals Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 83-95

Recently, the feed-forward neural network is functioning with slow computation time and increased gain. The weight vector and biases in the neural network can be tuned based on performing intelligent assignment for simple generalized operation. This drawback of FFNN is solved by using various ELM algorithms based on the applications issues. ELM algorithms have redesigned the existing neural networks with network components such as hidden nodes, weights, and biases. The selection of hidden nodes is randomly determined and leverages good accuracy than conservative methods. The main aim of this research article is to explain variants of ELM advances for different applications. This procedure can be improved and optimized by using the neural network with novel feed-forward algorithm. The nodes will mainly perform due to the above factors, which are tuning for inverse operation. The ELM essence should be incorporated to reach a faster learning speed and less computation time with minimum human intervention. This research article consists of the real essence of ELM and a briefly explained algorithm for classification purpose. This research article provides clear information on the variants of ELM for different classification tasks. Finally, this research article has discussed the future extension of ELM for several applications based on the function approximation.

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.


2022 ◽  
Author(s):  
Jiankang Wu ◽  
Shuai Zhang ◽  
Jiayue Xu ◽  
Junwu Dang ◽  
Qingyang Zhao ◽  
...  

Abstract The mammalian brain has an extremely complex, diversified and highly modular structure, and information dissemination in the modular brain network affects various brain diseases. Although a variety of neuromodulation techniques have been used to study the discharge characteristics of neural networks, the effects of transcranial magneto-acoustic electrical stimulation(TMAES) have rarely been mentioned. Based on the excitatory and inhibitory Izhikevich neuron model, we constructs a feed-forward neural network connected by electrical synapses and chemical synapses, and analyzes the firing frequency of the neural network under TMAES and magnetic stimulation and the differences in each layer types of firing patterns of neurons. The results showed that the discharge patterns of neurons in each layer were different, the discharge frequency of inhibitory neurons was higher than that of excited neurons, and the stimulation signal could be transmitted to the whole network layer.The maximum discharge frequency of neural network connected by electrical coupling can reach 0.94kHz, and the discharge frequency of neural network connected by chemical coupling is less than 0.5 kHz.With the increase of coupling degree, the discharge frequency of neurons in each network layer under TMAES is much greater than that under magnetic stimulation.When the induced current is less than 26.5μA/cm 2 , magnetic stimulation can promote the inhibitory neurons, and TMAES has a variety of regulatory effects on the inhibitory neurons in the neural network. The results show that TMAES has better regulation effect than magnetic stimulation, and the regulation effect is affected by neural network structure and stimulation parameters.


2016 ◽  
Vol 13 (10) ◽  
pp. 7538-7544
Author(s):  
T Jayasankar ◽  
J. Arputha Vijayaselvi

A Feed Forward Neural Network (FFNN) model primarily based unrestricted delivery prediction of language unit length pattern info speech synthesis system is that the focus of this paper. Estimation of delivery parameter of segmental length plays a essential half in unrestricted concatenative synthesis Text To Speech System (TTS) is capable of synthesize natural sounding speech with improved quality. Common options to coach the Neural Network enclosed language unit position within the phrase, context of language unit, language unit position within the word, language unit nucleus and amp; language unit identity square measure extracted from the text. Back-propagation Neural Network (BPNN) formula is one in every of the foremost wide used and a preferred technique to optimize the feed forward neural network coaching in delivery prediction. For enhance the accuracy of delivery prediction language unit length in neural BP, that’s Cuckoo Search formula to seek out the structure of the neural network with least weights while not compromising on the prediction error is planned. Speech information is adopted to check the length prediction performance of planned SOCNN, wherever the obtained results demonstrate a marked improvement over the essential BP. The system performance is shown mistreatment the synthesizing natural sounding speech for Tamil, national language of Republic of India.


Author(s):  
Radoslav Štrba ◽  
Daniela Bordencea

Wafer-defect maps can provide important information about manufacturing defects. The information can help to identify bottlenecks in the semiconductor manufacturing process. The main goal is to recognize random versus patterned defects. A patterned defect shows that a step in the process is not performed correctly. If same defect occurs multiple times, then the yield can rapidly decrease. This article proposes a method for yield improvement and defect recognition by using a feed-forward neural network. The neural network classifies wafer-defect maps into classes. Each class represents certain defect on the map. The neural network was trained, tested and validated using a wafer-defect maps dataset containing real defects inspired from manufacturing process.


2014 ◽  
Vol 1036 ◽  
pp. 995-1000 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu ◽  
Nicoleta Rachieru

This paper presents the combined application of Artificial Neural Networks and the RULA method in the process of redesign ergonomic workstations.Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to ranking a workstation. The neural network is simulated with a simple simulator: SSNN. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department, using CATIA V5 software.


Author(s):  
Michele Giorelli ◽  
Federico Renda ◽  
Gabriele Ferri ◽  
Cecilia Laschi

The solution of the inverse kinetics problem of soft manipulators is essential to generate paths in the task space to perform grasping operations. To address this issue, researchers have proposed different iterative methods based on Jacobian matrix. However, although these methods guarantee a good degree of accuracy, they suffer from singularities, long-term convergence, parametric uncertainties and high computational cost. To overcome intrinsic problems of iterative algorithms, we propose here a neural network learning of the inverse kinetics of a soft manipulator. To our best knowledge, this represents the first attempt in this direction. A preliminary work on the feasibility of the neural network solution has been proposed for a conical shape manipulator driven by cables. After the training, a feed-forward neural network (FNN) is able to represent the relation between the manipulator tip position and the forces applied to the cables. The results show that a desired tip position can be achieved quickly with a degree of accuracy of 0.73% relative average error with respect to total length of arm.


2021 ◽  
Vol 118 ◽  
pp. 103766
Author(s):  
Ahmed J. Aljaaf ◽  
Thakir M. Mohsin ◽  
Dhiya Al-Jumeily ◽  
Mohamed Alloghani

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