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A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data

Ping Guo, Michael R. Lyu
2004 Neurocomputing  
The algorithm is tested on case studies with stacked generalization applications to software reliability growth data.  ...  A supervised learning algorithm, Pseudoinverse Learning Algorithm (PIL), for feedforward neural networks is developed.  ...  Acknowledgements The authors wish to thank the anonymous reviewers for their useful suggestions and comments on the paper.  ... 
doi:10.1016/s0925-2312(03)00385-0 fatcat:fybj2vszcrf6nhpfounhrwkomi

Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification [article]

Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, Joaquín Huerta
2022 arXiv   pre-print
Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data.  ...  Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning.  ...  In order to reduce the training time, [7] proposed a new learning algorithm for Single hidden layer feedforward neural network (SLFN) called Extreme Learning Machine (ELM), which uses Moore-Penrose generalized  ... 
arXiv:2204.10418v1 fatcat:t5k47ahhbje7vaqcvlakzaguli

Multimodal Sparse Classifier for Adolescent Brain Age Prediction [article]

Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang
2019 arXiv   pre-print
Due to extremely large variable-to-instance ratio of PNC data, a high dimensional matrix with several irrelevant and highly correlated features is generated and hence a pattern learning approach is necessary  ...  We propose a sparse learner based on the residual errors along the estimation of an inverse problem for the extreme learning machine (ELM) neural network.  ...  PRELIMINARIES ELM algorithms, originally proposed by Huang et al. [14] , are single layer feedforward neural networks (SLFNs) [9, 10, 15] .  ... 
arXiv:1904.01070v1 fatcat:ybak7dutifaufg3ypkkaxinia4

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TII Aug. 2021 5208-5217 Modified Newton Integration Neural Algorithm for Dynamic Complex-Valued Matrix Pseudoinversion Applied to Mobile Object Localization.  ...  ., +, TII Dec. 2021 8541-8549 Modified Newton Integration Neural Algorithm for Dynamic Complex-Val-ued Matrix Pseudoinversion Applied to Mobile Object Localization.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

AI and ML – Enablers for Beyond 5G Networks

Alexandros Kaloxylos, Anastasius Gavras, Daniel Camps Mur, Mir Ghoraishi, Halid Hrasnica
2020 Zenodo  
A family of neural networks is presented, which are generally speaking, non-linear statistical data modelling and decision making tools.  ...  Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data.  ...  One step further, more accurate results in using deep learning algorithms to detect network anomalies more specifically leveraging the feedforward neural network (FNN) and convolutional neural network  ... 
doi:10.5281/zenodo.4299895 fatcat:ngzbopfm6bb43lnrmep6nz5icm

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Hossam Fraihat, Amneh A. Almbaideen, Abdullah Al-Odienat, Bassam Al-Naami, Roberto De Fazio, Paolo Visconti
2022 Future Internet  
The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database.  ...  In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8).  ...  Data Availability Statement: Data of our study are available upon request. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi14030079 fatcat:a76yjtfpkvbkpfxzzo3wuv4mi4

Operational Decision Support in the Presence of Uncertainties [article]

Corneliu T.C. Arsene
2018 arXiv   pre-print
This book addresses the scientific domains of operations research, information science and statistics with a focus on engineering applications.  ...  In general an operational DS comprises a series of standalone applications from which the mathematical modeling and simulation of the distribution systems and the managing of the uncertainty in the decision-making  ...  Acknowledgments The author would like to thank to Prof. Bogdan Gabrys from Bournemouth University  ... 
arXiv:1701.04681v3 fatcat:4u7pdqhyafakfkgzudn4svtg3a

Recent advances in mechatronics

Okyay Kaynak
1996 Robotics and Autonomous Systems  
The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or  ...  Four years has since then passed and in order to discuss the most recent advances, it has been decided to hold another similar conference during 24-  ...  Acknowledgements The authors would like to thank Mr. K. Tonegawa for his help with the experiments.  ... 
doi:10.1016/s0921-8890(96)00039-5 fatcat:l5fd4hwa2rbu3l6f2jucyj2fxy

Brain–Machine Interface Engineering

Justin C. Sanchez, José C. Principe
2007 Synthesis Lectures on Biomedical Engineering  
Scott Morrison, Shalom Darmanjian, and Greg Cieslewski developed and programmed the first portable systems for online learning of neural data. Later on, our colleagues Dr. Toshi Nishida and Dr.  ...  a journey of discovery in new theories for interfacing with the brain.  ...  The procedure for deriving the sensitivity for a feedforward topology is an application of the chain rule [26] .  ... 
doi:10.2200/s00053ed1v01y200710bme017 fatcat:jm6kaqyjurgddmssiru2fy435i

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Leonid L. Rubchinsky, Sungwoo Ahn, Wouter Klijn, Ben Cumming, Stuart Yates, Vasileios Karakasis, Alexander Peyser, Marmaduke Woodman, Sandra Diaz-Pier, James Deraeve, Eliana Vassena, William Alexander (+473 others)
2017 BMC Neuroscience  
states for general neural networks incorporating a fast and a slow sub- Here we describe a learning scheme for spiking neural networks that system denoting with 0 < ε<1 the difference in  ...  of this manuscript is the application of the generalized PRC to a realis- tic neural network with a dynamic feedback  ... 
doi:10.1186/s12868-017-0371-2 fatcat:jkqnv4rvfvaj5g44xm5snzdqzy

Certifying Unstability of Switched Systems Using Sum of Squares Programming

Benoît Legat, Pablo Parrilo, Raphaël Jungers
2020 SIAM Journal of Control and Optimization  
This alternative way of analysis of slow-fast systems with particular structure may lead to a later analysis of multi-timescale systems over networks.  ...  In a similar manner as for the linear case, we can compute a balanced realization from these two empirical Gramians.  ...  A NLMPC scheme with feedforward neural networks as predictive model, was useful for denitrification process [3] .  ... 
doi:10.1137/18m1173460 fatcat:ytlzbwk7vbampbuyo6snenz33m

Tutorial Review on Space Manipulators for Space Debris Mitigation

Ellery
2019 Robotics  
Space-based manipulators have traditionally been tasked with robotic on-orbit servicing or assembly functions, but active debris removal has become a more urgent application.  ...  We begin with a cursory review of on-orbit servicing manipulators followed by a short review on the space debris problem.  ...  neural networks.  ... 
doi:10.3390/robotics8020034 fatcat:6aorr2lxa5bdpfgpk7ttvsqbrq

Alternative Power Systems and Devices [chapter]

2017 Systems, Controls, Embedded Systems, Energy, and Machines  
Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their  ...  Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed.  ...  Profibus is available with data rates up to 12 Mbps. Train Communication Network (TCN).  ... 
doi:10.1201/9781420037043-7 fatcat:px3rpk2xgvhtbjwr7s3h3k6ute

Modeling and experiments with low‐frequency pressure wave propagation in liquid‐filled, flexible tubes

Cato Bjelland, Leif Bjo/rno/
1992 Journal of the Acoustical Society of America  
An artificial neural network model of human sound localization. T. R. Anderson (Armstrong Lab., AL/CFBA, Wright-Patterson AFB, Dayton, OH 45433-6573), J. A.  ...  A new design algorithm is proposed that is based on a logarithmic distortion metric and it is shown how this algorithm can be used to obtain minimum phase, low-order pole-zero approximations to HRTFs.  ...  A new method for metaphoric learning in neural networks is presented.  ... 
doi:10.1121/1.404777 fatcat:xhmwz65h5bbqxbt52khae2rq7q

Predicting room acoustical behavior with the ODEON computer model

Graham Naylor, Jens Holger Rindel
1992 Journal of the Acoustical Society of America  
An artificial neural network model of human sound localization. T. R. Anderson (Armstrong Lab., AL/CFBA, Wright-Patterson AFB, Dayton, OH 45433-6573), J. A.  ...  A new design algorithm is proposed that is based on a logarithmic distortion metric and it is shown how this algorithm can be used to obtain minimum phase, low-order pole-zero approximations to HRTFs.  ...  A new method for metaphoric learning in neural networks is presented.  ... 
doi:10.1121/1.404931 fatcat:z4hbezklfzgu7hyy7qvos3wyo4
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