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Ironmaking process modeling uncertainty quantification via conformal prediction based on random vector functional link networks

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Zhou, Ping Wen, Chaoyao Zhao, Peng Li, Mingjie Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China

For real-world industrial system modeling, dynamic stochastic errors inevitably exist in data- driven deterministic predictions (i.e., point predictions). The uncertainty of such prediction results directly affects various prediction-based operations for work condition identification and production decision-making. Therefore, a novel interval prediction method quantifying multi-output uncertainty is proposed by combining conformal prediction with random vector functional link networks (RVFLNs), which has fast learning speed and high accuracy performance. The proposed algorithm is used for the reliable prediction of molten iron quality in blast furnace ironmaking process. Firstly, to address the issue that shallow learning models have limited expression capabilities to describe complex nonlinear relationships, the dynamic attention mechanism and semi-supervised autoencoder are utilized to reveal and represent the correlations between different input variables and multi-output variables. Subsequently, the Elastic Net regularization technique is adopted to improve the multicollinearity and overfitting problems of traditional RVFLNs. Further, considering the deterioration of prediction accuracy and credibility caused by uncertain system dynamics, an Empirical Copula function-based Copula prediction uncertainty quantification method is introduced to realize multi-output variables reliable prediction with a given confidence level. Finally, actual blast furnace industrial data is applied to demonstrate the validity, utility, and sophistication of model.

关键词： Conformal prediction random vector functional link networks Uncertainty quantification Blast ironmaking process Quality prediction Semi-supervised autoencoder

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random vector functional link networks for Road Traffic Forecasting: Performance Comparison and Stability Analysis

Random Vector Functional Link Networks for Road Traffic Fore...

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International Joint Conference on Neural networks (IJCNN)

作者： Manibardo, Eric L. Lana, Ibai Del Ser, Javier TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain

ISBN: (纸本)9780738133669

Nowadays, Machine Learning algorithms enjoy a great momentum in multiple engineering and scientific fields. In the context of road traffic forecasting, the number of contributions resorting to these modeling techniques is increasing steadily over the last decade, in particular those based on deep neural networks. In parallel, randomization based neural networks have progressively garnered the interest of the community due to their learning efficiency and competitive predictive performance. Although these two properties are often sought for practical traffic forecasting solutions, randomization based neural networks have so far been scarcely investigated for this domain. In particular, the instability of these models due to the randomization of part of their parameters is often a deciding factor for discarding them in favor of other modeling choices. This research work sheds light on this matter by elaborating on the suitability of random vector functional link (RVFL) for road traffic forecasting. On one hand, multiple RVFL variants (single-layer RVFL, deep RVFL and ensemble deep RVFL) are compared to other Machine Learning algorithms over an extensive experimental setup, which comprises traffic data collected at diverse geographical locations that differ in the context and nature of the collected traffic measurements. On the other hand, the stability of RVFL models is analyzed towards providing insights about the compromise between model complexity and performance. The results obtained by the distinct RVFL approaches are found to be similar than those elicited by other data driven methods, yet requiring a much lower number of trainable parameters and thereby, drastically shorter training times and computational effort.

关键词： randomization based neural networks random vector functional link networks road traffic forecasting

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A comprehensive evaluation of random vector functional link networks

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INFORMATION SCIENCES 2016年 367卷 1094-1105页

作者： Zhang, Le Suganthan, P. N. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

With randomly generated weights between input and hidden layers, a random vector functional link network is a universal approximator for continuous functions on compact sets with fast learning property. Though it was proposed two decades ago, the classification ability of this family of networks has not been fully investigated yet. Through a very comprehensive evaluation by using 121 UCI datasets, the effect of bias in the output layer, direct links from the input layer to the output layer and type of activation functions in the hidden layer, scaling of parameter randomization as well as the solution procedure for the output weights are investigated in this work. Surprisingly, we found that the direct link plays an important performance enhancing role in RVFL, while the bias term in the output neuron had no significant effect. The ridge regression based closed-form solution was better than those with Moore-Penrose pseudoinverse. Instead of using a uniform randomization in [-1,+1] for all datasets, tuning the scaling of the uniform randomization range for each dataset enhances the overall performance. Six commonly used activation functions were investigated in this work and we found that hardlim and sign activation functions degenerate the overall performance. These basic conclusions can serve as general guidelines for designing RVFL networks based classifiers. (C) 2015 Elsevier Inc. All rights reserved.

关键词： random vector functional link networks Ridge regression Moore-Penrose pseudoinverse Data classification

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FPGA Implementation of random vector functional link networks based on Elementary Cellular Automata 35

FPGA Implementation of Random Vector Functional Link Network...

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35th Conference on Design of Circuits and Integrated Systems (DCIS)

作者： Moran, Alejandro Canals, Vincent Roca, Miquel Isern, Eugeni Rossello, Josep L. Univ Balearic Isl Dept Ind Engn & Construct Palma De Mallorca Spain

ISBN: (纸本)9781728191324

Today, there is an increasing demand in designing and deploying low-power Machine Learning (ML) models for edge applications such as mobile phones or specific purpose smart cameras and voice processing systems. These products might incorporate low power processors and ASICs to manage communications and meet power/energy consumption specifications in computationally intensive tasks. These ML tasks usually include Deep Learning systems that require a considerably number of multiply-accumulate (MAC) operations, thus impacting in both hardware and energy requirements. Here we show a novel ML hardware design optimized for edge applications that considerably reduces the number of MAC operations based on incorporating Elementary Cellular Automata within a random vector functional link Network architecture. As a proof of concept, the proposed system is implemented in a Field-Programmable Gate Array and applied to the well known MNIST dataset, showing high-performance characteristics along with a considerably higher energy efficiency if compared with recently published ML-hardware designs.

关键词： random vector functional link networks Cellular automata Digital circuits

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ONLINE CONTINUAL LEARNING USING ENHANCED random vector functional link networks 47

ONLINE CONTINUAL LEARNING USING ENHANCED RANDOM VECTOR FUNCT...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wong, Cheryl Sze Yin Yang, Guo Ambikapathi, Arulmurugan Savitha, Ramasamy ASTAR Inst Infocomm Res I2R 1 Fusionopolis Way Singapore 138632 Singapore

ISBN: (纸本)9781665405409

We propose an online continual learning algorithm based on an enhanced random vector functional link Network (OCL-eRVFL), that learns a sequence of tasks continually, where each task is defined by streaming data with each sample arriving once and only once. As data for a new task in domain incremental or class incremental setting streams in, the output weights of an eRVFL is updated through Recursive least squares, such that the representations for the past tasks are not catastrophically forgotten. As the recursive least square update is based only on the currently streaming sample, samples are not stored. Hence, unlike state-of-the-art OCL that avoid catastrophic forgetting through memory replay of samples from past task, the proposed OCL-eRVFL needs no extra memory. The proposed OCL-eRVFL is evaluated on streaming split CIFAR10, split CIFAR100 and split CIFAR10/100 image classification data sets within a class and domain incremental setting. Performance results show that the proposed OCL-eRVFL efficiently learns a sequence of tasks with streaming data, without additional memory expense.

关键词： random vector functional link networks recursive least squares online continual learning

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A Dual-Stream Deep Learning Architecture With Adaptive random vector functional link for Multi-Center Ischemic Stroke Classification

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IEEE ACCESS 2025年 13卷 46638-46658页

作者： Inamdar, Mahesh Anil Gudigar, Anjan Raghavendra, U. Salvi, Massimo Aman, Raja Rizal Azman Bin Raja Gowdh, Nadia Fareeda Muhammad Ahir, Izzah Amirah Binti Mohd Kamaruddin, Mohd Salahuddin Bin Kadir, Khairul Azmi Abdul Molinari, Filippo Hegde, Ajay Menon, Girish R. Acharya, U. Rajendra Manipal Acad Higher Educ Manipal Inst Technol Dept Mechatron Manipal 576104 India Manipal Acad Higher Educ Manipal Inst Technol Dept Instrumentat & Control Engn Manipal 576104 India Taylors Univ Sch Med Fac Hlth & Med Sci Subang Jaya 47500 Malaysia Taylors Univ Digital Hlth & Med Advancement Impact Lab Subang Jaya 47500 Malaysia Politecn Torino PolitoBIOMed Lab Dept Elect & Telecommun Biolab I-10129 Turin Italy Univ Malaya Univ Malaya Res Imaging Ctr Univ Malaya Med Ctr Fac MedDept Biomed Imaging Kuala Lumpur 50603 Malaysia Manipal Hosp Bengaluru 560017 India Manipal Acad Higher Educ Kasturba Med Coll Dept Neurosurg Manipal 576104 India Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Hlth Res Springfield Qld 4300 Australia

One of the main causes of death and permanent disability is ischemic stroke, for which prompt and precise diagnosis is essential to successful treatment. This study introduces a novel dual-stream deep learning framework for ischemic stroke classification using Computed Tomography (CT) images, specifically addressing challenges in accuracy, computational efficiency, and clinical interpretability. Three significant innovations are included in the suggested architecture: (1) a hybrid Dual Attention Mechanism that combines Dynamic Routing and Cross-Attention for improved region-specific feature discrimination;(2) a Multi-Scale Feature Extraction Module with parallel convolutional pathways that captures both contextual and fine-grained features;and (3) an Adaptive random vector functional link layer that significantly reduces training time while maintaining high classification performance. When tested on a single-center dataset, the model achieves state-of-the-art classification accuracy of 98.83% across normal, acute and chronic stroke categories. We demonstrate the strong generalization capabilities of the proposed framework by achieving 92.42% accuracy on a diverse, multi-center dataset of 7,842 CT images. The integration of explainable Artificial Intelligence tools improve clinical trustworthiness by offering clear insight into the model's decision-making process. These outcomes demonstrate the model's potential to use in actual clinical settings for quick and accurate stroke diagnosis, along with its interpretability and computational efficiency.

关键词： Computed tomography Feature extraction Accuracy Biomedical imaging Imaging Stroke (medical condition) Medical diagnostic imaging Lesions Magnetic resonance imaging Image segmentation Ischemic stroke detection deep learning cross-attention random vector functional link networks medical image analysis explainable AI

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An evolutionary features-based neural grey system model and its application

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APPLIED MATHEMATICAL MODELLING 2025年 145卷

作者： Ma, Xin Hao, Yiwu Li, Wanpeng Southwest Univ Sci & Technol Sch Math & Phys Mianyang Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen Scotland

Effectively addressing nonlinear small-sample data has been a critical focus in time-series research. Due to their "black-box" nature and strong reliance on data scale, traditional machine learning models often struggle with such datasets, prompting many studies to adopt neural grey models. However, existing neural grey models frequently suffer from overfitting and limited predictive accuracy. To address these issues, this study integrates the random vector functional link network into grey models for the first time and proposes five evolutionary feature-based training algorithms for the hybrid framework. Through evaluations on five real-world cases using four accuracy metrics, the proposed model which is based on moth-flame optimization algorithm demonstrates superior performance, achieving mean absolute percentage error improvements ranging from 6.5181% to 96.7559% compared to 21 grey models and 15 machine learning models. Additionally, the study reveals that increasing neuron counts and iteration steps enhances model complexity and training time, underscoring the importance of optimizing these parameters for improved performance. This research bridges the gap in random vector functional link network applications for small-sample data and partially alleviates the limitations of neural grey models in terms of overfitting and predictive accuracy.

关键词： Grey system model random vector functional link networks Evolutionary algorithm Least squares method Grid search algorithm

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A new learning paradigm for random vector functional-link network: RVFL

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NEURAL networks 2020年 122卷 94-105页

作者： Zhang, Peng-Bo Yang, Zhi-Xin Univ Macau Fac Sci & Technol State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Fac Sci & Technol Dept Electromech Engn Macau 999078 Peoples R China

In school, a teacher plays an important role in various classroom teaching patterns. Likewise to this human learning activity, the learning using privileged information (LUPI) paradigm provides additional information generated by the teacher to 'teach' learning models during the training stage. Therefore, this novel learning paradigm is a typical Teacher-Student Interaction mechanism. This paper is the first to present a random vector functional link (RVFL) network based on the LUPI paradigm, called RVFL+. The novel RVFL+ incorporates the LUPI paradigm that can leverage additional source of information into the RVFL, which offers an alternative way to train the RVFL. Rather than simply combining two existing approaches, the newly-derived RVFL+ fills the gap between classical randomized neural networks and the newfashioned LUPI paradigm. Moreover, the proposed RVFL+ can perform in conjunction with the kernel trick for highly complicated nonlinear feature learning, termed KRVFL+. Furthermore, the statistical property of the proposed RVFL+ is investigated, and the authors present a sharp and high-quality generalization error bound based on the Rademacher complexity. Competitive experimental results on 14 real-world datasets illustrate the great effectiveness and efficiency of the novel RVFL+ and KRVFL+, which can achieve better generalization performance than state-of-the-art methods. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： RVFL KRVFL Learning using privileged information The Rademacher complexity SVM random vector functional link networks

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Electricity Load Demand Time Series Forecasting with Empirical Mode Decomposition based random vector functional link Network

Electricity Load Demand Time Series Forecasting with Empiric...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Qiu, Xueheng Suganthan, P. N. Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Cambridge Dept Engn Cambridge England

ISBN: (纸本)9781509018970

Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and random vector functional link network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.

关键词： Ensemble learning Load demand time series forecasting random vector functional link networks Neural networks Support vector regression

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Adaptive Control of Discrete-time Nonlinear Systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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