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Jacobi Neural Network Method for Solving Linear Differential-Algebraic Equations with Variable Coefficients

NEURAL PROCESSING LETTERS 2021年第5期53卷 3357-3374页

作者： Liu, Hongliang Liu, Huini Xu, Jie Li, Lijuan Song, Jingwen Xiangtan Univ Sch Math & Computat Sci Hunan Key Lab Computat & Simulat Sci & Engn Xiangtan 411105 Peoples R China Xiangtan Univ Sch Automat & Elect Informat Xiangtan 411105 Peoples R China

A novel Jacobi neural network method is proposed for solving linear differential-algebraic equations (DAEs) in the paper. First, Jacobi neural network is applied to derive the approximate solutions form of DAEs, and the loss function is constructed for DAEs based on single hidden layer Jacobi neural network structure. Then, we get the optimal output weights of Jacobi neural network by applying extreme learning machine algorithm. In particular, Legendre neural network method and Chebyshev neural network method which have been widely used by scholars are special cases of Jacobi neural network method, and the numerical results of the proposed method are better than these of Legendre neural network method and Chebyshev neural network method. Furthermore, Jacobi neural network method has higher accuracy compared with the approximate analytical methods, the numerical comparison results further show the feasibility and effectiveness of the proposed method for solving the DAEs.

关键词： Convergence Differential-algebraic equations extreme learning machine algorithm Jacobi polynomials Neural network

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IoT-based approach to condition monitoring of the wave power generation system

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IET RENEWABLE POWER GENERATION 2019年第12期13卷 2207-2214页

作者： Qian, Peng Feng, Bo Zhang, Dahai Tian, Xiange Si, Yulin Brunel Univ London Dept Mech Aerosp & Civil Engn Uxbridge UB8 3PH Middx England Zhejiang Univ Ocean Coll Hangzhou 310058 Zhejiang Peoples R China

Accurate and reliable fault detecting plays a key role in application of grid-connected wave power generation systems. This study presents a novel IoT-based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform;and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT-based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications.

关键词： wave power generation fault diagnosis wave power plants condition monitoring Internet of Things learning (artificial intelligence) power grids grid-connected wave power generation systems cloud platform IoT-based wave power generation condition monitoring system test platform compressed sensing method CS method extreme learning machine algorithm ELM algorithm

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Proactive frequency control based on ultra-short-term power fluctuation forecasting for high renewables penetrated power systems

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IET RENEWABLE POWER GENERATION 2019年第12期13卷 2166-2173页

作者： Wen, Shuli Wang, Yu Tang, Yi Xu, Yan Li, Pengfei Nanyang Technol Univ Energy Res Inst 50 Nanyang Ave Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore Singapore

The rapidly increasing penetration of renewable energies has introduced severe challenges to power system frequency controls due to the highly intermittent and uncertain power output of renewable energies. This study proposes a proactive frequency control method to control traditional synchronous generators in advance in anticipation of sudden power fluctuation. Therefore, the frequency deviations can be well mitigated by such early-acted control signal. In order to obtain this new control reference, an ensemble-forecasting model based on the extreme learning machine algorithm is designed to predict ultra-short-term power fluctuations, which serves as an extra signal for automatic generation control. The proposed method was verified on an equivalent model of the Singapore power system with various types of generations and loads. The simulation results clearly demonstrate the accuracy of the forecasting model and the advantages of the proposed control method. The proposed method can also reduce the frequency charging/discharging of energy storage systems, which can effectively extend their lifetime.

关键词： energy storage distributed power generation wind power plants battery storage plants synchronous generators power generation control power system control frequency control learning (artificial intelligence) power grids control reference ensemble-forecasting model extreme learning machine algorithm ultra-short-term power fluctuations automatic generation control Singapore power system generations loads frequency charging discharging energy storage systems ultra-short-term power fluctuation forecasting high renewables power systems rapidly increasing penetration renewable energies severe challenges power system frequency controls highly intermittent power output uncertain power output proactive frequency control method traditional synchronous generators sudden power fluctuation frequency deviations control signal

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Improving Image Classification Accuracy With ELM and CSIFT

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COMPUTING IN SCIENCE & ENGINEERING 2019年第5期21卷 26-34页

作者： Li, Qing Peng, Qiang Chen, Junzhou Yan, Chuan Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Sichuan Peoples R China

We present a new image classification system by integrating color information and the extreme learning machine algorithm based on the localized soft-assignment coding classification framework. Our system is original with its unique structure. It improves the image classification accuracy significantly compared to the existing image classification systems. Specifically, on classification accuracy, our system can improve more than 4% on the Caltech-101 dataset and achieve up to 3% improvement on the Caltech-256 dataset compared to existing systems.

关键词： Image Classification Image Colour Analysis learning Artificial Intelligence Transforms Image Classification Accuracy Image Classification System Color Information extreme learning machine algorithm Localized Soft Assignment Coding Classification Framework Encoding Image Coding Feature Extraction Training Image Color Analysis Histograms Visualization Classification Color Information extreme learning machine Image Classification

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A multilayer perceptron neural network approach for the solution of hyperbolic telegraph equations

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2021年第2-4期32卷 65-82页

作者： Panghal, Shagun Kumar, Manoj Motilal Nehru Natl Inst Technol Allahabad Prayagraj 211004 India

Neural networks have been extensively used for solving differential equations in the past, but they rely mostly on computationally expensive gradient-based numerical optimization procedure for solving differential equations. In this work, we are introducing a faster way to train neural networks for solving differential equations based on extreme learning machine algorithm. This algorithm is much faster as compared to traditional approaches, and it also provides highly accurate results. Reliability of the approach is tested by solving various cases of the hyperbolic telegraph equations. Solutions so obtained are compared to the results existing in the literature for analysing the accuracy of the proposed approach.

关键词： Multilayer perceptron extreme learning machine algorithm hyperbolic telegraph equation

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SLeNN-ELM: A shifted Legendre neural network method for fractional delay differential equations based on extreme learning machine

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NETWORKS AND HETEROGENEOUS MEDIA 2023年第1期18卷 494-512页

作者： Ye, Yinlin Li, Yajing Fan, Hongtao Liu, Xinyi Zhang, Hongbing Northwest A&F Univ Coll Sci Yangling 712100 Shaanxi Peoples R China

In this paper, we introduce a shifted Legendre neural network method based on an extreme learning machine algorithm (SLeNN-ELM) to solve fractional differential equations with constant and proportional delays. Based on the properties of Caputo fractional derivatives and shifted Legendre polynomials, the fractional derivatives of SLeNN can be represented analytically without other numerical techniques. SLeNN, in terms of neural network architecture, uses a function expansion block to replace the hidden layer, and thus improving the computational efficiency by reducing parameters. In terms of solving technology of neural networks, the extreme learning machine algorithm is used to replace the traditional gradient-based training algorithm. It dramatically improves our solution efficiency. In addition, the proposed method does not require parameter initialization randomly, making the neural network solution stable. Finally, three examples with constant delays and three examples with proportional delays are given, and the effectiveness and superiority of the proposed method are verified by comparison with other numerical methods.

关键词： Fractional delay differential equation Constant delay and proportional delay Shifted Legendre neural network extreme learning machine algorithm

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Research on data-driven assessment of power outage losses for power users 24

Research on data-driven assessment of power outage losses fo...

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International Conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Tang, Bo Wang, Guan Fan, Chuiyu Zhao, Wanning Yang, Boyu State Grid Shenyang Elect Power Supply Co Shenyang Peoples R China

ISBN: (纸本)9798400716638

This paper starts by systematically analyzing the factors that affect user power outage losses based on the electricity consumption characteristics, outage characteristics, and distribution network operation characteristics. It constructs a factor system for the influence of user power outage losses based on eight indicators from both the distribution network side and the user side. Additionally, it delves into the composition components of power outage losses for different types of users and explains the source and processing of assessment *** paper proposes a power outage loss assessment method based on the extreme learning machine (ELM) algorithm and validates this method using comprehensive data from user samples for the years 2020-2023. The results show that the proposed method in this paper exhibits significant effectiveness compared to existing assessment methods in the literature, with a relatively small deviation from actual loss data.

关键词： Data-Driven extreme learning machine algorithm User Power Outage Loss Distribution Network Side and User Side

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A Novel GA-ELM approach for Parkinson's disease detection using Brain Structural T1-weighted MRI Data 2

A Novel GA-ELM approach for Parkinson's disease detection us...

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2nd International Conference on Cognitive Computing and Information Processing (CCIP)

作者： Pahuja, Gunjan Nagabhushan, T. N.

ISBN: (纸本)9781509010257

Parkinson's disease is the second most common neurodegenerative disorder caused by progressive loss of dopamine in substantia nigra. Various techniques like Magnetic Resonance Imaging (MRI), functional MRI (fMRI), and Positron emission tomography (PET) could be used to enumerate the loss of neurons in different parts of brain. In this paper we present a novel approach for detecting PD using brain MRI scans. Because of non-invasiveness and high resolution property, MRI is preferred over other techniques. For this study, the MRI images (healthy/PD patients) have been collected from Parkinson's Progression Markers Initiative (PPMI) organization. Research efforts have stated that extreme learning machine (ELM) has better and accurate diagnosis ability. In this paper, PD diagnosis based on ELM-based method along with Genetic algorithm feature subset selection has been proposed. The classifier uses voxel based morphometric features extracted from MRI. Since, the feature extracted are large in number, a feature subset selection technique using Genetic algorithm is implemented. The performance of GA-ELM method is evaluated using classification accuracy, sensitivity, specificity. The results show that the classification accuracy obtained for ELM model is higher than the one obtained using SVM approach. Also GA-ELM classifier model produces a better generalization performance with high sensitivity and low misclassification rate.

关键词： extreme learning machine algorithm Genetic algorithm Magnetic Resonance Imaging (MRI) Optimization Parkinson's disease Support Vector machine Voxel-based Morphometry

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Detecting Malicious Tweets in Trending Topics using Clustering and Classification 4

Detecting Malicious Tweets in Trending Topics using Clusteri...

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International Conference on Recent Trends in Information Technology

作者： Soman, Saini Jacob Murugappan, S. Sathyabama Univ Fac CSE Madras Tamil Nadu India Annamalai Univ DDE Dept Comp Sci & Engn Madras Tamil Nadu India

ISBN: (纸本)9781479949892

Detection of spam twitter social networks is one of the significant research areas to discover unauthorized user accounts. A number of research works have been carried out to solve these issues but most of the existing techniqueshad not focused on various features and doesn't group similar user trending topics which become their major limitation. Trending topics collects the current Internet trends and topics of argument ofeach and every user. In order to overcome the problem of feature extraction, this work initially extracts many features such as user profile features, user activity features, location based features and text and content features. Then the extracted text features use Jenson-Shannon Divergence (JSD) measure to characterize each labeled tweet using natural language models. Different features are extracted from collected trending topics data in twitter. After features are extracted, clusters are formed to group similar trending topics of tweet user profile. Fuzzy K-means (FKM) algorithm primarily cluster the similar user profiles with same trending topics of tweet and centers are determined to similar user profiles with same trending topics of tweet from fuzzy membership function. Moreover, extreme learning machine (ELM) algorithm is appliedtoanalyze the growing characteristics of spam with similar topics in twitter from clustering result and acquire necessary knowledge in the detection of spam. The results are evaluated with F-measure, True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy with improved detection results.

关键词： extreme learning machine algorithm Fuzzy K-Means Clustering algorithm Spam detection Social network

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Heart Disease Classification using Crow Search algorithm 5

Heart Disease Classification using Crow Search Algorithm

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Maragatharajan, M. Naveen, P. Nirmala, E. Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

This work introduces cutting-edge research to predict heart diseases. This application designed for accurate and personalized assessment of cardiovascular health. This work aims to provide an overall understanding of an individual's heart health by integrating these multifaceted data points. This work utilizes a vast dataset to establish correlations and patterns, enabling it to categorize users into specific risk groups or diagnose potential heart diseases. Through continuous learning and adaptation, the work refines its predictive capabilities, ensuring that it remains up-to-date with emerging medical knowledge. The work not only assists in early detection of cardiovascular issues but also aids in personalized risk assessment, guiding users towards lifestyle modifications and preventive measures based on their unique health profiles. This innovative solution holds the potential to revolutionize the field of cardiovascular health assessment, offering a proactive and data-driven approach to identify and manage heart diseases efficiently. This study considered two well-known machine learning models namely extreme learning machine algorithms (ELM) and Crow search algorithm (CSA). The collected dataset consists of 10000 samples. We have attained 92.5% accuracy with the data set. The incorporation of machine learning models ensures scalability, adaptability, and precision, making it a valuable tool for both healthcare professionals and individuals striving for proactive heart care. © 2024 IEEE.

关键词： Accuracy Crow search algorithm Data preprocessing extreme learning machine algorithm Feature selection Heart diseases

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