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RFBLS: A robust rough fuzzy broad learning system with local neighborhood structure

Neurocomputing 2025年 647卷

作者： Chu, Yonghe Guo, Yanlong Li, Peng Ding, Weiping Pedrycz, Witold Cao, Heling School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Ministry of Education Zhengzhou450001 China Centre for Complexity Science Henan University of Technology Zhengzhou450001 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology Macau SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

As an extension of random vector functional link neural network (RVFLNN), the broad learning system (BLS) has gradually been applied in various fields due to its advantages of simple network structure, rapid training, and fast model updating. Despite its remarkable achievements, there are still some difficulties and challenges that restrict its further application in practical tasks. In BLS, all samples equally contribute to constructing the optimal classification boundary. However, when training samples contain noise or outliers, it will result in the obtained classification boundary not being the true optimal classification boundary. In other words, BLS is sensitive to noise points or outliers. To address it, we propose a robust rough fuzzy broad learning system with local neighborhood structure (RFBLS). In RFBLS, fuzzy sets are utilized by associating a membership degree with each sample. This incorporation ensures diverse contributions of different samples during the minimization of the objective function. Samples with noise or outliers are assigned smaller weights, effectively eliminating the influence of noise and outlier samples in the learning process. We employ the rough set theory to explore the influence-decision degree of each conditional attribute on the decision attribute, aiming to eliminate the impact of redundant or interfering features. Subsequently, we comprehensively consider the distance between samples and class centers, as well as the local neighborhood structure relationship between samples. Initially, we calculate the initial membership degree of each sample based on the distance to class centers. Then, utilizing fuzzy K-nearest neighbors, we calculate and rank the membership degrees of K nearest neighbors for each sample. These rankings are then weighted with the initial membership degree, achieving the fusion of relationships and membership degree calculations between samples. Finally, extensive comparative experiments are conducted on var

关键词： Fuzzy set theory

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Event-Triggered Monitoring/Communication of Inertial Measurement Unit for IoT Applications

Event-Triggered Monitoring/Communication of Inertial Measure...

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IEEE Sensors

作者： Mohammad Salimibeni Parvin Malekzadeh Mohammadamin Atashi Mihai Barbulescu Konstantinos N. Plataniotis Arash Mohammadi Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Toronto Canada

Communication of high-frequency and equidistantly sampled Inertial Measurement Unit (IMU) measurements is impractical as it drains power of the handhold device and even may result in crashing the phone's operating system. In this regard, the paper proposes a novel Event-Triggered (ET) IMU monitoring/communication strategy for indoor localization in IoT applications. This is, to the best of our knowledge, the first work to develop ET methodologies within this context where communication only occurs when specific events are identified via a local triggering mechanism implemented at the sensor level. Different experimental scenarios are implemented illustrating effectiveness of the proposed ET-based IMU communication.

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Data Clustering using Variational Learning of Finite Scaled Dirichlet Mixture Models 28

Data Clustering using Variational Learning of Finite Scaled ...

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28th IEEE International Symposium on Industrial Electronics, ISIE 2019

作者： Nguyen, Hieu Azam, Muhammad Bouguila, Nizar Concordia Institute for Information Systems Engineering Concordia University MontrealH3G 1M8 Canada Department of Electrical and Computer Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9781728136660

We introduce a variational Bayesian learning framework for finite Scaled Dirichlet mixture model. The variational inference estimates the parameters without the computational cost of conventional Bayesian methods. Furthermore, our model avoids including additional model selection criterion by automatically determining the optimal number of components as a part of the inference process. The proposed model is tested with real-life data clustering problems to demonstrate its effectiveness. © 2019 IEEE.

关键词： Unsupervised learning

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Gaussian Mixture-based Indoor Localization via Bluetooth Low Energy Sensors

Gaussian Mixture-based Indoor Localization via Bluetooth Low...

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IEEE Sensors

作者： Parvin Malekzadeh Mohammad Salimibeni Mohammadamin Atashi Mihai Barbulescu Konstantinos N. Plataniotis Arash Mohammadi Department of Electrical and Computer Engineering (ECE) Concordia University Montreal Canada Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Toronto Canada

A probabilistic Gaussian mixture model (GMM) of the Received Signal Strength Indicator (RSSI) is proposed to perform indoor localization via Bluetooth Low Energy (BLE) sensors. More specifically, to deal with the fact that RSSI-based solutions are prone to drastic fluctuations, GMMs are trained to more accurately represent the underlying distribution of the RSSI values. For assigning real-time observed RSSI vectors to different zones, first a Kalman Filter is applied to smooth the RSSI vector and form its Gaussian model, which is then compared in distribution with learned GMMs based on Bhattacharyya distance (BD) and via a weighted K-Nearest Neighbor (K-NN) approach.

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A comparative study of eight human auditory models of monaural processing

arXiv

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arXiv 2021年

作者： Vecchi, Alejandro Osses Varnet, Léo Carney, Laurel H. Dau, Torsten Bruce, Ian C. Verhulst, Sarah Majdak, Piotr Laboratoire des systèmes perceptifs Département d'études cognitives École Normale Supérieure PSL University CNRS Paris France Departments of Biomedical Engineering and Neuroscience University of Rochester RochesterNY United States Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark Department of Electrical and Computer Engineering McMaster University HamiltonON Canada Hearing Technology group WAVES Department of Information Technology Ghent University Ghent Belgium Acoustics Research Institute Austrian Academy of Sciences Vienna Austria

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models. © 2021, CC BY-NC-SA.

关键词： Physiological models

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Corrigendum to “Numerical analysis of the magnetic field impact on hydrothermal characteristics of a microchannel heatsink with Fe 3 O 4 ferrofluid and various pin-fin shapes” [J. Magn. Magn. Mater. 585 (2023) 171102]

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Journal of Magnetism and Magnetic Materials 2024年 610卷

作者： Tao Hai Kamal Sharma Riyam K. Marjan Babak Farhang Mohamed H. Mahmoud Hassan Fouad Walid El-Shafai School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China School of Electronics and Information Engineering Ankang University China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 China Institute of Engineering and Technology GLA University Mathura UP 281406 India Department of Intelligent Medical Systems Al-Mustaqbal University College Babylon 51001 Iraq Department of Energy Technology Aalborg University Denmark Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo P.O. Box 11795 Egypt Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

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Gradient descent in materia through homodyne gradient extraction

arXiv

引用

arXiv 2021年

作者： Boon, Marcus N. Cassola, Lorenzo Euler, Hans-Christian Ruiz Chen, Tao van de Ven, Bram Ibarra, Unai Alegre Bobbert, Peter A. van der Wiel, Wilfred G. NanoElectronics Group MESA+ Institute for Nanotechnology BRAINS Center for Brain-Inspired Nano Systems University of Twente PO Box 217 Enschede7500 AE Netherlands Exzellenzcluster Science of Intelligence Technische Universität Berlin Marchstr. 23 Berlin10587 Germany Department for Electrical Engineering and Computer Science Modeling of Cognitive Processes Technische Universität Berlin Berlin Germany Molecular Materials and Nanosystems Eindhoven Institute for Renewable Energy Systems Department of Applied Physics Eindhoven University of Technology PO Box 513 Eindhoven5600 MB Netherlands Institute of Physics University of Münster Münster48149 Germany

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning [1]. One of its key enablers has been backpropagation [2], an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use in gradient descent [3]. However, the implementation of deep learning in digital computers is intrinsically energy hungry, with energy consumption becoming prohibitively high for many applications [4]. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits [5] [6, 7] and integrated photonic tensor cores [8] to unconventional, material-based computing systems [9] [10] [11] [12] [13] [14] [15] [16]. The learning process in these material systems, realized, e.g., by artificial evolution [13] [14], equilibrium propagation [17] or surrogate modelling [18] [19], is a complicated and time-consuming process. Here, we demonstrate a simple yet efficient and accurate gradient extraction method, based on the principle of homodyne detection, for performing gradient descent on a loss function directly in a physical system without the need of an analytical description. By perturbing the parameters that need to be optimized using sinusoidal waveforms with distinct frequencies, we effectively obtain the gradient information in a highly robust and scalable manner. We illustrate the method in dopant network processing units [14], but argue that it is applicable in a wide range of physical systems. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems. © 2021, CC BY-NC-SA.

关键词： CMOS integrated circuits

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Partial Caging: A Clearance-Based Definition and Deep Learning

Partial Caging: A Clearance-Based Definition and Deep Learni...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Anastasiia Varava Michael C. Welle Jeffrey Mahler Ken Goldberg Danica Kragic Florian T. Pokomy Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of technology varavar AUTOLab Department of Electrical Engineenng and Computer Science University of California Berkeley

Caging grasps limit the mobility of an object to a bounded component of configuration space. We introduce a notion of partial cage quality based on maximal clearance of an escaping path. As this is a computationally demanding task even in a two-dimensional scenario, we propose a deep learning approach. We design two convolutional neural networks and construct a pipeline for real-time partial cage quality estimation directly from 2D images of object models and planar caging tools. One neural network, CageMaskNN, is used to identify caging tool locations that can support partial cages, while a second network that we call CageClearanceNN is trained to predict the quality of those configurations. A dataset of 3811 images of objects and more than 19 million caging tool configurations is used to train and evaluate these networks on previously unseen objects and caging tool configurations. Furthermore, the networks are trained jointly on configurations for both 3 and 4 caging tool configurations whose shape varies along a 1-parameter family of increasing elongation. In experiments, we study how the networks' performance depends on the size of the training dataset, as well as how to efficiently deal with unevenly distributed training data. In further analysis, we show that the evaluation pipeline can approximately identify connected regions of successful caging tool placements and we evaluate the continuity of the cage quality score evaluation along caging tool trajectories. Experiments show that evaluation of a given configuration on a GeForce GTX 1080 GPU takes less than 6 ms.

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Data-Driven Model Order Reduction for T-Product-Based Dynamical systems

arXiv

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arXiv 2025年

作者： Mei, Shenghan He, Ziqin Mei, Yidan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems the Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics the Department of Biostatistics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

— Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of applications, including robotic systems, signal processing, and fluid dynamics. However, traditional model order reduction techniques like balanced truncation are not designed to handle tensor data directly and instead require unfolding the data, which may lead to the loss of important higher-order structural information. In this article, we introduce a novel framework for data-driven model order reduction of T-product-based dynamical systems (TPDSs), which are often used to capture the evolution of third-order tensor data such as images and videos through the T-product. Specifically, we develop advanced T-product-based techniques, including T-balanced truncation, T-balanced proper orthogonal decomposition, and the T-eigensystem realization algorithm for input-output TPDSs by leveraging the unique properties of T-singular value decomposition. We demonstrate that these techniques offer significant memory and computational savings while achieving reduction errors that are comparable to those of conventional methods. The effectiveness of the proposed framework is further validated through synthetic and real-world *** Codes 93C05, 15A69, 93B30, 65F99 © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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