Bidirectional Extreme Learning Machine Research Articles

A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis

Predicting and analysing flight delays is essential for successful air traffic management and control. We propose a novel parallel-series model and novel adaptive bidirectional extreme learning machine (AB-ELM) method for prediction and feature analysis to better understand the causes of flight delays as stated by the International Air Transport Association (IATA). The IATA-coded flight delays are rarely examined in the existing studies. The IATA-coded flight delay subcategories decision boundaries are improved by the proposed parallel-series model. In application areas, where multiclass-multilabel classification may produce erroneous performance, the parallel-series model can be regarded as an alternate strategy. To improve network generalization performance, the proposed AB-ELM optimizes the covariance objective function by altering the learning rate adaptively during gradient ascent as opposed to gradient descent. The historical data from one of Hong Kong's international airlines, which contains information about the airport, flight, aircraft, weather, and IATA flight delay subcategories is considered a case study. Using fourteen different sampling approaches, the influence of imbalanced and noisy data was reduced. The results showed that employing proper sampling approaches in conjunction with the parallel-series model and AB-ELM method is effective for uncovering hidden patterns in the complicated IATA-coded flight delay subcategories system. When compared to other data-driven approaches, AB-ELM attained a high accuracy of 80.66 percent. This study enables airlines to develop adequate contingency measures in advance based on potential flight delay reasons and duration.

A Novel Machine Learning Approach for Detection of Coronary Artery Disease Using Reduced Non-linear and Chaos Features

In this research paper, authors present an automated system in this paper that integrates a ranking technique with Principal Component Analysis (PCA), Generalized Discriminant Analysis (GDA) and a 1-Norm Bidirectional Extreme Learning Machine (1-NBELM) to reliably classify normal and coronary artery disease groups. Twenty chaotic and non-linear attributes were hauling out from the Heart Rate Variability (HRV) signal to detect coronary artery disease groups. The HRV data for this study derived from a typical database of Normal Old (ELY), Young (YNG), and Coronary Artery Disease (CAD) people. Fisher, Wilcoxon and Bhattacharya were used to compute the rankings of attributes. GDA then turned the ranking features into a new feature. The Radial Basis Function (RBF) kernel was used to transfer original features to a high-dimensional feature space in GDA and PCA, and then it was deployed to 1-NBELM, which utilized the sigmoidal or multiquadric non-linear activation. Numerical experiments were performed on the combination of database sets as Young-ELY, Healthy-CAD, and Healthy ELY-CAD subjects. The numerical results show that ROC with GDA and 1-NBELM approach achieved an accuracy of 98.12±0.14, 96.21±0.12 and 99.87±0.28 for Young-CAD, Young-ELY and Healthy ELY-CAD groups with the use of sigmoidal and multiquadric activation function. The Fisher with GDA and 1-NBELM and Bhattacharya with GDA and 1-Norm Extreme Learning Machine (1-NELM) approach achieved an accuracy of 99.98±0.21 for all databases.

Optimized Covariance Bidirectional Extreme Learning Machine for Prediction and Feature Analysis of Iata Coded Flight Delays Subcategories

Optimized Covariance Bidirectional Extreme Learning Machine for Prediction and Feature Analysis of Iata Coded Flight Delays Subcategories

Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines

Accurate prediction of the aircraft trajectory and associated fuel consumption has become an important research topic owing to the increasing importance of air traffic management. Currently, trajectory prediction and fuel estimation are usually accomplished via complex mathematical energy-balance methods. In these methods, the prediction error could get increased due to the possible usage of global values and outdated database, resulting from that most of the information regarding aircraft operations is unavailable. In this paper, we propose a covariance bidirectional extreme learning machine (CovB-ELM) for predicting aircraft trajectories and estimating fuel consumption. The selection of randomly generated parameters for the hidden unit, such as the input weight and bias, to improve the accuracy and numerical stability of the extreme learning machine (ELM), is an open problem. The fundamental idea behind the proposed method is to maximise the covariance between the hidden unit and network errors through partially updating the hidden-unit parameters randomly generated in bidirectional ELM so that the output weight norm value is minimised and the convergence gets improved. The merits of the proposed CovB-ELM are demonstrated by the experiments involving regression problems and international airline historically flight data, which suggests that the CovB-ELM outperforms, in terms of generalisation performance, several existing methods, e.g., airline mathematical approach, backpropagation neural network, and constructive ELM methods.

Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

The challenge in addressing uncalibrated visual servoing (VS) control of robot manipulators with unstructured environments is to obtain appropriate interaction matrix and keep the image features in the field of view (FOV), especially when the non-Gaussian noise disturbance exists in the VS process. In this article, a hybrid control algorithm which combines bidirectional extreme learning machine (B-ELM) with smooth variable structure filter (SVSF) is proposed to estimate interaction matrix and tackle visibility constraints. For VS, the nonlinear mapping between image features and interaction matrix is approximated using the B-ELM learning. To increase the capability of anti-interference, the SVSF is employed to re-estimate interaction matrix. A constraint function presenting feature coordinates and region boundaries is given and added to the velocity controller, which drags image features away from the restricted region and ensures the smoothness of the velocities. Since the camera and robot model parameters are not required in developing the control strategy, the servoing task can be fulfilled flexibly and simply. Simulation and experimental results on a conventional 6-degree-of-freedom manipulator verify the effectiveness of the proposed method.

A Novel fuzzy frame selection based watermarking scheme for MPEG-4 videos using Bi-directional extreme learning machine

With the advancement of Internet Technology, a lot of multimedia content is shared over the Internet resulting in several concerns over multimedia content like copyright protection, content authentication, and ownership. In this paper, we have developed a novel watermarking scheme for Advanced Video Coding (MPEG-4 AVC) videos using Bi-directional Extreme Learning Machine (B-ELM). Relevant frames to be watermarked are identified by implementing a newly developed fuzzy algorithm based on threshold operation. A binary watermark, encrypted by transposition cipher, is used to ensure enhanced security. The proposed watermarking scheme has been tested on five benchmark videos (Akiyo, Hall_Monitor, Mother_Daughter, Pamphlet, and Silent). High average Peak Signal to Noise Ratio (PSNR) values of the signed video sequences indicate good visual quality post-embedding. Common video interferences such as scaling, noising, cropping, filtering, frame dropping, frame averaging, and frame swapping are used to evaluate the robustness of the watermark embedding scheme. The high values of Normalized Correlation (NC(W,W′)) and Bit-Error-Rate (BER(W,W′)) between the original watermark (W) and the recovered watermark (W′) establish the good robustness of the proposed scheme. While the established algorithms such as Fuzzy Inference System (FIS), Back Propagation Network (BPN), Meta-heuristic techniques take time of the order of several seconds to process a frame, the proposed algorithm consumes only a few milliseconds. Thus, it is well suited for the real-time watermarking of compressed videos.

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real time. Empirical mode decomposition is used to decompose the nonstationary PQEs into the monocomponent mode of oscillation, known as intrinsic mode functions (IMFs). The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs. An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine, least-square support vector machine, extreme learning machine, and bidirectional extreme learning machine. The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform associated with HT and WBELM method, and tunable-Q wavelet transform along with HT and WBELM method. The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system. Finally, a hardware prototype is developed based on the digital signal processor to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.

Modified bidirectional extreme learning machine with Gram–Schmidt orthogonalization method

Abstract Incremental extreme learning machine has been proved to be an efficient and simple universal approximator. However, the network architecture may be very large due to the inefficient nodes which have a tiny effect on reducing the residual error. More to the point, the output weights are not the least square solution. To reduce such inefficient nodes, a method called bidirectional ELM (B-ELM), which analytically calculates the input weights of even nodes, was proposed. By analyzing, B-ELM can be further improved to achieve better performance on compacting structure. This paper proposes the modified B-ELM (MB-ELM), in which the orthogonalization method is involved in B-ELM to orthogonalize the output vectors of hidden nodes and the resulting vectors are taken as the output vectors. MB-ELM can greatly diminish inefficient nodes and obtain a preferable output weight vector which is the least square solution, so that it has better convergence rate and a more compact network architecture. Specifically, it has been proved that in theory, MB-ELM can reduce residual error to zero by adding only two nodes into network. Simulation results verify these conclusions and show that MB-ELM can reach smaller low limit of residual error than other I-ELM methods.

Intra ELM variants ensemble based model to predict energy performance in residential buildings

Abstract Optimization of energy performance of the buildings has been a great interest of research. Energy performance monitoring and maintenance depend on the several parameters such as temperature, humidity, sunlight, roof area, wall area and mostly on heating ventilation and air conditioning(HVAC). Each building has its own patterns and settings obtained through monitoring which depends on many parameters such as users activities, environmental related attributes and building design structural parameters etc. In this respect, an estimation of energy load of the building in real-time efficiently for optimization becomes an important task for cost-effective energy management. The paper proposes an Intra ensemble model based on variants of emerging machine learning approach which includes extreme learning machine(ELM), Online Sequential ELM(OSELM) and Bidirectional ELM(B-ELM). The energy performance estimation requires the model to be real-time and efficient. This asks for use of highly correlated parameters and a very efficient model. For this, OSELM based model for real-time prediction of energy performance has been used. ELM variants are used because of their fast computation and efficiency of prediction over conventional machine learning models. The proposed model has been compared with few state of art methods on accuracy and efficiency criteria and proposed models outperformed existing methods.

Back propagation bidirectional extreme learning machine for traffic flow time series prediction

On account of transportation management, a predictive model of the traffic flow is built up that would precisely predict the traffic flow, reduce longer travel delays. In prediction model of traffic flow based on traditional neural network, the parameters of prediction model need to be tuned through iterative processing, and these methods easily get stuck in local minimum. The paper presents a novel prediction model based on back propagation bidirectional extreme learning machine (BP-BELM). Parameters of BP-BELM are not tuned by experience. Compared with back propagation neural network, radial basis function, support vector machine and other improved incremental ELM, the combined simulations and comparisons demonstrate that BP-BELM is used in predicting the traffic flow for its suitability and effectivity.

Fast discrete s-transform and extreme learning machine based approach to islanding detection in grid-connected distributed generation

This paper presents an approach for islanding detection in distributed generation systems (DGs) using fast discrete s-transform (FDST) algorithm and bidirectional extreme learning machine (BELM) classifier. The system undertaken for this study comprises of two different kinds of DGs such as hydro turbine governor system and wind turbine generator. The analysis starts with extracting the non-stationary negative sequence voltage and current signals at DG end and then the instantaneous amplitude and frequency information are extracted through FDST algorithm. From this information, different distinguishing features are computed such as energy and standard deviation of amplitude to track the islanded event from different non-islanded events. The obtained features are examined through an extreme learning machine classifier to discriminate islanding and non-islanding events, under various operating conditions of distribution system. The accuracy of the proposed method is compared with other recently published techniques by various researchers to justify its improved performance for islanding detection.

Bi-directional extreme learning machine for semi-blind watermarking of compressed images

Bi-directional Extreme Learning Machine (B-ELM) is a newly developed single layer feed-forward network capable of fast training with few hidden neurons. It is also reported to show better generalization capability as compared to its old counterpart ELM. In the past, it has never been applied to image processing data-sets and particularly to any of its applications. In this work, B-ELM is successfully used to carry out watermarking of JPEG compressed images by inserting a binary watermark into it. Two invertible activation functions – Sine and Sigmoid are tested in this work. The RMSE is plotted as a function of number of hidden neurons. As observed in case of other applications, this plot indicates that Sigmoid is better placed in comparison to Sine function. The robustness of embedding scheme is examined by applying seven different attacks over signed images. These results prove that the proposed scheme is robust enough against the selected attacks. The computed processing time for embedding and extraction in milliseconds indicates that this scheme is suitable for developing real time watermarking applications for videos.

Improved bidirectional extreme learning machine based on enhanced random search

The incremental extreme learning machine (I-ELM) was proposed in 2006 as a method to improve the network architecture of extreme learning machines (ELMs). To improve on the I-ELM, bidirectional extreme learning machines (B-ELMs) were developed in 2012. The B-ELM uses the same method as the I-ELM but separates the odd and even learning steps. At the odd learning step, a hidden node is added like I-ELM. At the even learning step, a new hidden node is added via a formula based on the former added node result. However, some of the hidden nodes generated by the I-ELM may play a minor role; thus, the increase in network complexity due to the B-ELM may be unnecessary. To avoid this issue, this paper proposes an enhanced B-ELM method (referred to as EB-ELM). Several hidden nodes are randomly generated at each odd learning step, however, only the nodes with the largest residual error reduction will be added to the existing network. Simulation results show that the EB-ELM can obtain higher accuracy and achieve better performance than the B-ELM under the same network architecture. In addition, the EB-ELM can achieve a faster convergence rate than the B-ELM, which means that the EB-ELM has smaller network complexity and faster learning speed than the B-ELM.

Verification and predicting temperature and humidity in a solar greenhouse based on convex bidirectional extreme learning machine algorithm

Predictions regarding the solar greenhouse temperature and humidity are important because they play a critical role in greenhouse cultivation. On account of this, it is important to set up a predictive model of temperature and humidity that would precisely predict the temperature and humidity, reducing potential financial losses. This paper presents a novel temperature and humidity prediction model based on convex bidirectional extreme learning machine (CB-ELM). Simulation results show that the convergence rate of the bidirectional extreme learning machine (B-ELM) can further be improved while retaining the same simplicity, by simply recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. The performance of the CB-ELM model is compared with other modeling approaches by applying it to predict solar greenhouse temperature and humidity. The experiment results show that the CB-ELM model predictions are more accurate than those of the B-ELM, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Radial Basis Function (RBF). Therefore, it can be considered as a suitable and effective method for predicting the solar greenhouse temperature and humidity.

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and high-dimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.