Back Propagation Convex Extreme Learning Machine

Zou, Weidong; Yao, Fenxi; Zhang, Baihai; Guan, Zhitao

doi:10.1007/978-3-319-57421-9_21

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Haykin and Network conducted a comprehensive review of the numerous applications of neural network architecture in the development of cognitive frameworks for solving engineering problems. It was found that generally three types of training algorithms were implemented with satisfactory results for the development of engineering models, namely, Propagation algorithms like QP, Back Propagation, Incremental Back Propagation and many similar algorithms; Gradient Descent algorithms like CGD and Special training algorithms like Levenberg Marquardt Algorithms . In engineering problems mostly, the Feed forward, feedback and recurrent neural network architecture were used.…”

Section: Methods Usedmentioning

confidence: 99%

Optimization of material removal rate in wire EDM by polynomial neural network models

Dutta

Majumder

Panja

2019

Computational Intelligence

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Maximization of material removal rate may yield impractical results if the cost for generation of removal rate is not included in the optimization. Presently, there are many studies which have applied different linear, nonlinear, and metaheuristics optimization techniques to maximize material removal rate but none of the approaches have the capability of self‐adaptation. In none of these studies impact of cost on removal rate was considered. That is why, in the present study, the Group Method of Data Handling which is a technique to develop polynomial neural network (PNN) models was utilized for the first time to maximize material removal rate with the help of the on‐ and off‐stage current as the design variables constrained by the operating costs. The PNN architecture is widely used in many fields of technology and science. However, application of this architecture is scarce in case of optimization of material removal rate. According to the results the accuracy level and time of convergence in case of PNN was found to be better compared to that from artificial neural network and linear models both of which were utilized for estimation of the output variable.

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Section: Methods Usedmentioning

confidence: 99%

Optimization of material removal rate in wire EDM by polynomial neural network models

Dutta

Majumder

Panja

2019

Computational Intelligence

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“…In this work, the algorithm applied for multilayer feed-forward neural networks [9][10] is the back-propagation learning algorithm [11]. These neurons in this network are sequenced into layers, since human brain is assumed to have strata-like pattern.…”

Section: A Multi-layer Feed-forward Neural Network (Mlffnn)mentioning

confidence: 99%

Pixel Based Sar Image Classification using Random Forest Algorithm

Balnarsaiah¹,

Prasad²,

Laxminarayana³

2019

IJITEE

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Synthetic Aperture Radar (SAR) images (Microwave data) were classified using Multi-Layer Feed Forward, Cascade Forward Neural Networks and Random Forest (RF) algorithms. For the Random Forest, a general model for classification of Remotely Sensed Radar dual-polarization data based on RF is implemented and classified of SAR image (microwave data) classifications. The RF model exploits spatial context between neighbouring pixels in an image, and temporal class dependencies between different images of the same region, in the case of multi-temporal data. Based on the well-founded experimental on basis of random forest techniques for classification tasks and the encouraging experimental results in RF algorithm , the authors conclude that the proposed RF algorithm is useful for classification of SAR (Sentinel 1A) imagery and evaluate its accuracy and kappa coefficient.

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Extreme Learning Machine Based Model Improved with Adaptive Activation Functions

Fonseca

Goliatt

2021

Advances in Intelligent Systems and Computing

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Back Propagation Convex Extreme Learning Machine

Cited by 7 publications

References 12 publications

Optimization of material removal rate in wire EDM by polynomial neural network models

Optimization of material removal rate in wire EDM by polynomial neural network models

Pixel Based Sar Image Classification using Random Forest Algorithm

Extreme Learning Machine Based Model Improved with Adaptive Activation Functions

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