Random Vector Functional Link Neural Network Research Articles

Low voltage series arc fault identification method based on VMD and RVFL neural network

Abstract In low-voltage distribution system, series arc fault is one of the main causes of fire accidents. The series arc current characteristics of load are similar to its normal working current, and the ordinary current protection device cannot detect it effectively. In this paper, an arc recognition method based on variational modal decomposition (VMD) and random vector functional link (RVFL) neural network is proposed. The series arc current data of different loads are collected by the low-voltage series arc experimental platform, and the arc current characteristics are analyzed. Four different intrinsic mode functions (IMF) are obtained by variational modal decomposition, and their fuzzy entropy is calculated respectively to form an eigenvector data set, which is input into RVFL neural network for training and identification. In this paper, approximate entropy (ApEn), sample entropy (SampEn) and fuzzy entropy (FuzzyEn) are extracted as feature vectors for each IMF of VMD, their discrimination for different loads is analyzed, and the recognition accuracy of RVFL neural network compared with SVM and BP neural network algorithm is compared. It is verified that the arc recognition method proposed in this paper has better recognition effect.

Application of nonlinear system identification for EEG modelling using VMD-based deep random vector functional link network

In this paper, the EEG signal is considered for the development of the model. As the signal is nonlinear and non-stationary, the model is designed accordingly which is similar to nonlinear dynamic system identification. Initially, the signal is decomposed by a robust variational mode decomposition method for which the basic noise components are eliminated. Later, a kurtosis index method is applied to choose the best band-limited intrinsic mode functions (BLIMFs) based on their clean coefficient the model is developed using a random vector functional link neural network (RVFLN) for identification. The use of deep RVFLN provides better results as compared to simple RVFLN as explained in the result section. For verification of the system's robustness, three different epileptic signals known as pre-ictal, inter-ictal and ictal are experienced in this piece of work.

Bayesian neural network with efficient priors for online quality prediction

In process industries, neural networks are popular models for quality prediction due to their superiorities on dealing with nonlinearity and high dimensionality of process data. However, small sample size of online data will result in epistemic uncertainty of prediction model, so neural networks with fixed parameters are prone to overfitting and provide over-confident predictions with poor robustness. To this end, we propose a Pior-Assistant Random Bayesian Neural Network (PA-RBNN). First, the Random Vector Functional Link Neural Network (RVFLNN) with Bayes backpropagation (RBNN) is introduced to the last layer of the off-lined deep neural network model. The prior mean values and variances of parameters are determined by the closed-form solution from RVFLNN. Then, the semi-supervised form of PA-RBNN is further developed by introducing manifold regularization to RVFLNN to obtain the semi-supervised prior means and variances of parameters. In this way, the proposed prior provides a reasonable range before learning the parameter posterior, and both the labeled and unlabeled data are utilized to obtain the prior. The proposed method is verified by the benchmarked industrial process data. The experimental results show that the assistant prior has an optimistic effect on guiding the training process towards the basins of attraction of minima with less computation cost, and improves the generalization of prediction model.

A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network

The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.

Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis

Random vector functional link neural network (RVFLNN) is an effective and powerful neural network model, and it has been commonly used for various engineering applications. In this paper, an improved RVFLNN model called discriminative manifold RVFLNN (DMRVFLNN) is proposed and applied for rolling bearing fault diagnosis, which has the following outstanding characteristics. First, DMRVFLNN relaxes the strict one-hot label matrix into a soft label matrix to fully exploit the interclass discriminative information and simultaneously enlarge the distances between interclass samples. Second, a within-class similarity graph based on manifold learning is designed for DMRVFLNN to force interclass samples to keep close together as far as possible. In the construction process of the within-class similarity graph, cosine distance is used to measure the distances between samples to enhance its robustness. Moreover, we design an effective update method solution for DMRVFLNN with a closed-form solution in each iteration. Two rolling bearing datasets are used to verify the fault diagnosis performance, and the experimental results prove the effectiveness and superiority of the proposed method for rolling bearing fault diagnosis.

A Novel Functional Link Network Stacking Ensemble with Fractal Features for Multichannel Fall Detection

Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.

A new Exponentially Expanded Robust Random Vector Functional Link Network based MPPT model for Local Energy Management of PV-Battery Energy Storage Integrated Microgrid

In this paper a new Maximum Power Point Tracking (MPPT) model is presented for Local Energy Management (LEM) of a multiple Photovoltaic (PV) based microgrid. To detect accurate MPP references under local uncertainties, a non-iterative Linear Recurrence Relationship (LRR) based PV model is incorporated with PV penetration index. A robust, accurate and fast Exponentially Expanded Robust Random Vector Functional Link network (EE-RRVFLN) based MPPT algorithm is constructed with an exponentially expansion unit to address positive dynamic volatility and a direct link relationship to address null vs. positive volatility in PV data. The robustness is further incorporated by a maximum likelihood estimator using Huber’s cost function, where both input and output weights are optimally estimated by targeting reduction in MPP tracking error. An Assessment Index (i.e. MPPT error related) based Distributed Adaptive Droop (DAD) mechanism is suggested as Primary Controller (PC) for effective power sharing among multiple PVs. A detailed case study is presented to evaluate the accuracy of the proposed model in MATLAB simulation, as well as in dSPACE 1104 based Hardware-in-Loop (HIL) platform. Historical data for different intervals/ seasons, partial shading, improved LEM validations (simulation and HIL) are considered as different cases to establish the excellence of the proposed approach, as compared with conventional Functional Link Neural Network (FLNN) and Random Vector Functional Link Neural Network (RVFLNN).

Short-term solar power prediction using multi-kernel-based random vector functional link with water cycle algorithm-based parameter optimization

A new hybrid model combining the kernel functions along with the random vector functional link neural network (RVFLN) is proposed in this paper for an effective solar power prediction. The conventional RVFLN is known for its fast learning speed, simple architecture and good generalization capabilities and allows direct connection between input and output nodes along with nonlinear enhancement nodes with random weights. However, the bottleneck of selecting the number of hidden enhancement nodes and mapping functions is still a challenging problem. To overcome these deficiencies of the conventional RVFLN, kernel functions are used for both the direct links and the hidden nodes to provide better stability, generalization and regression accuracy. Instead of using a single kernel for the enhancement nodes, this paper proposes an optimal kernel function that comprises a linear combination of weighted local kernel and a global kernel to improve the prediction accuracy of the solar power generation. This optimal kernel will be known as multi-kernel RVFLN (MK-RVFLN), and its parameters are optimized using an efficient metaheuristic evaporation-based water cycle (EVWCA-MKRVFLN) to provide accurate prediction of solar power. To validate its superior prediction accuracy, two solar power plants of 25 and 100 MW capacity in the states of New York and California are considered for 5-min- and 60-min-ahead prediction in the months of January, April, July and October. The result analysis shows that the MK-RVFLN algorithm attains better performance than many other techniques.

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.

Rapid temperature prediction method for electronic equipment cabin

An accurate model is very important for thermal prediction and thermal management of airborne electronic module. Thermal Network Model (TNM) is the commonly used to analyze transient thermal response. It cannot describe the nonlinear or time-varying temperature process very well. In order to realize a fast thermal modeling with a relatively high accuracy, this paper proposes a modeling method based on sliding time window Random Vector Functional Link Neural Network (RVFLNN). The input and output variables of RVFLNN are determined by analyzing the heat transfer relationship of studied system. This method can rapidly realize thermal modeling without a time-consuming iterative training process. In order to overcome the variability of studied system, the sliding time window technology is specially introduced to improve the model prediction accuracy. This method is applied to analyze thermal experimental data of electronic equipment cabin. The temperature prediction performance of presented method is compared with the traditional Artificial Neural Network Model (ANNM). Comparison results show that the proposed modeling method has the advantages of fast modeling and good prediction accuracy. This study can provide an effective way to describe a complex dynamic heat transfer process adaptively and accurately, which may help the thermal control scheme design.

A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression

The Random Vector Functional Link Neural Network (RVFLNN) enables fast learning through a random selection of input weights while learning procedure determines only output weights. Unlike Extreme Learning Machines (ELM), RVFLNN exploits connection between the input layer and the output layer which means that RVFLNN are higher class of networks. Although RVFLNN has been proposed more than two decades ago (Pao, Park, Sobajic, 1994), the nonlinear expansion of the input vector into set of orthogonal functions has not been studied. The Orthogonal Polynomial Expanded Random Vector Functional Link Neural Network (OPE-RVFLNN) utilizes advantages from expansion of the input vector and random determination of the input weights. Through comprehensive experimental evaluation by using 30 UCI regression datasets, we tested four orthogonal polynomials (Chebyshev, Hermite, Laguerre and Legendre) and three activation functions (tansig, logsig, tribas). Rigorous non-parametric statistical hypotheses testing confirms two major conclusions made by Zhang and Suganthan for classification (Zhang and Suganthan, 2015) and Ren et al. for timeseries prediction (Ren, Suganthan, Srikanth, Amaratunga, 2016) in their RVFLNN papers: direct links between the input and output vectors are essential for improved network performance, and ridge regression generates significantly better network parameters than Moore-Penrose pseudoinversion. Our research shows a significant improvement of network performance when one uses tansig activation function and Chebyshev orthogonal polynomial for regression problems. Conclusions drawn from this study may be used as guidelines for OPE-RVFLNN development and implementation for regression problems.

Intrusion signal recognition in OFPS under multi-level wavelet decomposition based on RVFL neural network

The performance of the optical fiber pre-warning system (OFPS) is susceptible to the interference of background noise, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a recognition method based on the multi-level wavelet decomposition is proposed to accurately identify the running and digging intrusion signals in OFPS under the low SNR condition. The method includes the cross-correlation operation, feature extraction, and recognition using a random vector functional-link (RVFL) neural network (NN). Firstly, the collected signals are processed by the cross-correlation operation. Compared with the conventional filtering method, the background noise can be suppressed to the greatest extent by the cross-correlation operation, which keeps the signal details as much as possible. Secondly, the cross-correlation functions obtained from the cross-correlation operation are decomposed into five levels by the multi-level wavelet decomposition. And then the average energy ratios can be obtained along with the decomposed levels, and we select these ratios in the five frequency bands as the feature of intrusion signals. Next, the feature samples are sent into the RVFL NN for training so as to complete the recognition of these intrusion signals. Finally, the effectiveness of the algorithm is verified by the field experiment.