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random vector functional link network with subspace-based local connections

APPLIED INTELLIGENCE 2023年第2期53卷 1567-1585页

作者： He, Yu-Lin Yuan, Zhen-Hao Huang, Joshua Zhexue Shenzhen Univ Coll Comp Sci Big Data Inst Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

A new random vector functional link (RVFL) network with subspace-based local connections (abbreviated as RVFL-SLC network) is proposed in this paper. The main innovation of RVFL-SLC network is that the local connections are utilized in the input-layer of RVFL network. To select appropriate local connections for RVFL-SLC network, a novel efficient decision entropy criterion is used to partition the original attribute space into disjoint attribute subspaces, where the different attribute subspaces correspond to the respective hidden-layer weights. The decision entropy of each attribute subspace is first defined based on the posterior probabilities of classes given the attribute subspace. Then, the attribute subspaces are iteratively updated to increase the validation accuracy of the RVFL-SLC network. In each iteration, the attribute subspace with the minimum decision entropy is selected as candidate for updating. The optimal attribute subspaces are finally obtained when the RVFL-SLC network reaches a stable validation accuracy. To evaluate the classification accuracy of RVFL-SLC, it was compared on 20 benchmark data sets with four other randomization-based networks, namely the regular RVFL network, regular extreme learning machine (ELM), ELM with local connections (ELM-LC), and the No-Prop network. Experimental results show that the proposed RVFL-SLC network not only obtains the highest testing accuracy compared to other randomization-based networks but also best prevents overfitting. This demonstrates the rationality and effectiveness of using the strategy of subspace-based local connections to improve the generalization capability of RVFL networks.

关键词： randomization-based learning random vector functional link network Attribute grouping Attribute subspace Local connections Decision entropy

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Improved random vector functional link network with an enhanced remora optimization algorithm for predicting monthly streamflow

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JOURNAL OF HYDROLOGY 2025年 650卷

作者： Adnan, Rana Muhammad Mostafa, Reham R. Wang, Mo Parmar, Kulwinder Singh Kisi, Ozgur Zounemat-Kermani, Mohammad Guangzhou Univ Coll Architecture & Urban Planning Guangzhou 510006 Peoples R China Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura 35516 Egypt IKG Punjab Tech Univ Dept Math Kapurthala India Tech Univ Lubeck Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Sch Technol Tbilisi 0162 Georgia Shahid Bahonar Univ Kerman Dept Water Engn Kerman Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea

Precise and robust streamflow estimation is crucial for effective water resource management, particularly in mitigating extreme climatic events such as droughts and floods. This study introduces an innovative integration of the random vector functional link (RVFL) network with an Enhanced Remora Optimization Algorithm (EROA), specifically designed for monthly streamflow prediction. The RVFL-EROA is compared against standalone RVFL and RVFL models optimized using the Gorilla Troops Optimizer (GTO), Whale Optimization Algorithm (WOA), and the original Remora Optimization Algorithm (ROA). Performance is evaluated using statistical indices, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash-Sutcliffe Efficiency (NSE). The methodology is tested on streamflow time series data from the Kunhar River Basin in Pakistan, with input variables derived from antecedent streamflow, air temperature, and rainfall. Results indicate that temperature and streamflow-based inputs yielded higher accuracy compared to rainfall inputs. The RVFL-EROA outperformed other models, achieving improvements in mean RMSE, MAE, R2, and NSE by 8.63-1.77%, 12.08-1.58%, 16.88-3.33%, and 19.03-3.05%, respectively. Moreover, the RVFLEROA demonstrated superior performance in estimating peak streamflow values, which is critical for flood management. These findings highlight the potential of temperature-based inputs and RVFL-EROA models for streamflow prediction in data-scarce regions, particularly in developing countries. The proposed approach offers a reliable solution for enhancing hydrological forecasting and supports efficient water resource planning.

关键词： Streamflow prediction random vector functional link network Enhanced remora optimization algorithm Hydroclimatic data Gorilla troops optimizer Whale optimization algorithm

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An Elitist Artificial Electric Field Algorithm Based random vector functional link network for Cryptocurrency Prices Forecasting

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IEEE ACCESS 2023年 11卷 57693-57716页

作者： Nayak, Sarat Chandra Das, Subhranginee Dehuri, Satchidananda Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad 500075 India Fakir Mohan Univ Dept Comp Sci Balasore 756019 India

Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE

关键词： Computational efficiency Cryptocurrency bitcoin random vector functional link network financial time series forecasting artificial neural network AEFA

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Wireless network Slice Assignment With Incremental random vector functional link network

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2023年第3期10卷 1283-1296页

作者： He, Yulin Ye, Xuan Cui, Laizhong Fournier-Viger, Philippe Luo, Chengwen Huang, Joshua Zhexue Suganthan, Ponnuthurai Nagaratnam Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518060 Peoples R China Shenzhen Univ Big Data Inst Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Qatar Univ Coll Engn KINDI Ctr Comp Res Doha 2713 Qatar

This paper presents an artificial intelligence-assisted network slice prediction method, which utilizes a novel incremental random vector functional link (IRVFL) network to deal with the wireless network slice assignment (WNSA) problem in a data-driven way. The goal of WNSA is to assign an appropriate network slice for a user's requirement based on next-generation communication data. The IRVFL network is an incremental version of the RVFL network, where a data stream processing approach is used to gradually update output layer weights as new data arrive rather than processing the data as a single large data set. To ensure that the RVFL network can be trained for WNSA and have high adaptability and expansibility, we derive a novel flexible and appropriate rule for updating output layer weights of the IRVFL network. We have carried out extensive experiments to validate the feasibility, rationality, and effectiveness of using the IRVFL network for the WNSA problem. Results show that network slice prediction converges as the IRVFL network is incrementally trained and that the time required for training the incremental RVFL network is far less than for the non-incremental RVFL network. The incremental training of IRVFL network improves the performance of wireless network slice prediction. In addition, a comparison with six classification algorithms reveal that the IRVFL network consumes the least amount of time and has equivalent wireless network slice prediction performance.

关键词： Wireless networks network slicing Quality of service Next generation networking Modulation Medical services Fourth Industrial Revolution 5G network 6G network wireless network slice random vector functional link network Moore-Penrose generalized inverse incremental learning data stream computation

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Theoretical bounds of generalization error for generalized extreme learning machine and random vector functional link network

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NEURAL networkS 2023年第1期164卷 49-66页

作者： Kim, Meejoung Korea Univ Res Inst Informat & Commun Technol Seoul South Korea

Ensuring the prediction accuracy of a learning algorithm on a theoretical basis is crucial and necessary for building the reliability of the learning algorithm. This paper analyzes prediction error obtained through the least square estimation in the generalized extreme learning machine (GELM), which applies the limiting behavior of the Moore-Penrose generalized inverse (M-P GI) to the output matrix of ELM. ELM is the random vector functional link (RVFL) network without direct input to output links Specifically, we analyze tail probabilities associated with upper and lower bounds to the error expressed by norms. The analysis employs the concepts of the L2 norm, the Frobenius norm, the stable rank, and the M-P GI. The coverage of theoretical analysis extends to the RVFL network. In addition, a criterion for more precise bounds of prediction errors that may give stochastically better network environments is provided. The analysis is applied to simple examples and large-size datasets to illustrate the procedure and verify the analysis and execution speed with big data. Based on this study, we can immediately obtain the upper and lower bounds of prediction errors and their associated tail probabilities through matrices calculations appearing in the GELM and RVFL. This analysis provides criteria for the reliability of the learning performance of a network in real-time and for network structure that enables obtaining better performance reliability. This analysis can be applied in various areas where the ELM and RVFL are adopted. The proposed analytical method will guide the theoretical analysis of errors occurring in DNNs, which employ a gradient descent algorithm.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Generalized extreme learning machine random vector functional link network Generalization error Theoretical bound Tail probability Moore-Penrose generalized inverse

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Modified added activation function based exponential robust random vector functional link network with expanded version for nonlinear system identification

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APPLIED INTELLIGENCE 2022年第5期52卷 5657-5683页

作者： Samal, Debashisa Dash, Pradipta Kishore Bisoi, Ranjeeta Siksha O Anusandhan Deemed Univ Dept Elect & Commn Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Multidisciplinary Res Cell Bhubaneswar 751030 Odisha India

This paper presents an improved non-iterative random vector functional link network hybrid model with better input-output representation, improved generalization for nonlinear dynamic system identification. The modified random vector functional link network model uses random weights between the input layer and hidden or enhancement layer neurons whose outputs are obtained by using two suitably weighted activation functions and additionally it provides a weighted direct link between the trigonometric functions based exponentially expanded the inputs and the output node. This novel architecture provides a direct link of inputs and its nonlinear expanded version in a higher dimensional space to the output node along with a randomized version of the hidden layer operating with an optimized added activation function to handle the chaotic nature of the non-linear dynamic systems. Also the weights and the parameters associated with the summed activation functions are optimized using an efficient modified sine cosine algorithm based monarch butterfly algorithm with levy distribution optimization algorithm with better exploitation and exploration capabilities in order to improve overall identification accuracy. To authenticate the efficiency of the proposed model, five benchmark dynamic plants are examined;the achieved outputs are compared with recognized methods like extreme learning machine, conventional random vector functional link network, and enhanced random vector functional link network with single activation function and least mean square. The method proposed here exhibits improved performance accuracy which is superior to the considered models. The proposed model is also compared with some iterative existing methods and found suitable by taking into consideration the merits of non-iterative approach,

关键词： Nonlinear system identification random vector functional link network Added activation function based enhanced random vector functional link network Trigonometric exponential input enhancement Monarch butterfly optimization algorithm

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Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer

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JOURNAL OF INTELLIGENT MANUFACTURING 2021年第5期32卷 1377-1388页

作者： Elsheikh, Ammar H. Shehabeldeenz, Taher A. Zhou, Jianxin Showaib, Ezzat Abd Aziz, Mohamed Tanta Univ Fac Engn Dept Prod Engn & Mech Design Tanta 31527 Egypt Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430047 Peoples R China Kafrelsheikh Univ Fac Engn Dept Mech Engn Kafrelsheikh 33516 Egypt Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt

In this paper, an enhanced random vector functional link network (RVFL) algorithm was employed to predict kerf quality indices during CO(2)laser cutting of polymethylmethacrylate (PMMA) sheets. In the proposed model, the equilibrium optimizer (EO) is used to augment the prediction capability of RVFL via selecting the optimal values of RVFL parameters. The predicting model includes four input variables: gas pressure, sheet thickness, laser power, and cutting speed, and five kerf quality indices: rough zone ratio, widths of up and down heat affected zones, maximum surface roughness, and kerf taper angle. The experiments were designed using Taguchi L18 orthogonal array. The kerf surface contains three main zones: rough, transient, and smooth zones. The results of conventional RVFL as well as modified RVFL-EO algorithms were compared with experimental ones. Seven statistical criteria were used to assess the performance of the proposed algorithms. The results indicate that the RVFL-EO model has the predicting ability to estimate the laser-cutting characteristics of PMMA sheet.

关键词： Laser cutting Kerf quality indices random vector functional link network Equilibrium optimizer

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Modeling of drilling process of GFRP composite using a hybrid random vector functional link network/parasitism-predation algorithm

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2021年 14卷 298-311页

作者： Elsheikh, Ammar H. Abd Elaziz, Mohamed Ramesh, B. Egiza, Mohamed Al-qaness, Mohammed A. A. Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Saveetha Inst Med & Tech Sci Inst Mech Engn Saveetha Sch Engn Chennai 602105 Tamil Nadu India Kafrelsheikh Univ Mech Engn Dept Kafrelsheikh 33516 Egypt

Non-laminated glass fiber-reinforced epoxy composites (GFREC) have shown promising applications in various engineering fields. In this study, an experimental investigation followed by artificial intelligent modeling is carried out on the drilling of GFREC under two cooling conditions, namely internal and external. The damage factor of the drilled holes and the temperature of the drill tip were considered as main process responses. The ex-periments were conducted under different combinations of feed, spindle speed and coolant pressure. All holes were drilled using fresh tungsten carbide twist drills coated by TiN/TiAlN layer. A fine-tuned random vector functional link network (RVFL) model incor-porated with a new optimizer called parasitism-predation algorithm (PPA) was developed to model the drilling process. PPA acts as an advanced metaheuristic optimizer to obtain the best model variables of RVFL. The developed model was compared with the stand-alone RVFL as well as the fined-tuned RVFL using particle swarm optimizer (PSO). RVFL-PPA outperformed other models in terms of coefficient of determination, root mean square error and other measures. The coefficient of determination of RVFL-PPA for different responses and cooling conditions ranges between 0.995 and 1. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Non-laminated composites Glass fiber-reinforced epoxy Drilling Parasitism-predation algorithm random vector functional link network

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Wind power prediction using random vector functional link network with capuchin search algorithm

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AIN SHAMS ENGINEERING JOURNAL 2023年第9期14卷

作者： Al-qaness, Mohammed A. A. Ewees, Ahmed A. Fan, Hong Abualigah, Laith Elsheikh, Ammar H. Abd Elaziz, Mohamed Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Univ Bisha Coll Comp & Informat Technol Dept Informat Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon

Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations' efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The random vector functional link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive eval-uation experiments using public datasets from four wind turbines located in France, using several eval-uation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the pre-diction capability of the RVFL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： Wind power prediction Time series forecasting random vector functional link network Capuchin search algorithm (CapSA)

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Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2023年第3期17卷 816-839页

作者： Zhang, Xiaochun Zhou, Zhiyu Wenzhou Polytech Sch Intelligent Mfg Chashan Higher Educ Pk Wenzhou Zhejiang Peoples R China Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou Peoples R China

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

关键词： Grey Wolf Optimizer Moth Flame Optimizer random vector functional link network Illumination Correction

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