Single Layer Feedforward Network Research Articles

Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-Xylene Coatings Using Artificial Neural Network

The drying process of polymeric coatings, particularly in the presence of surfactants, poses a complex challenge due to its intricate dynamics involving simultaneous heat and mass transfer. This study addresses the inherent complexity by employing Artificial Neural Networks (ANNs) to model the surfactant-enhanced drying of poly(styrene)-p-xylene coatings. A substantial dataset of 16,258 experimentally obtained samples forms the basis for training the ANN model, showcasing the suitability of this approach when ample training data is available. The chosen single-layer feed-forward network with backpropagation adeptly captures the non-linear relationships within the drying data, providing a predictive tool with exceptional accuracy. Our results demonstrate that the developed ANN model achieves a precision level exceeding 99% in predicting coating weight loss for specified input values of time, surfactant amount, and initial coating thickness. The model’s robust generalization capability eliminates the need for additional experiments, offering reliable predictions for both familiar and novel conditions. Comparative analysis reveals the superiority of the ANN over the regression tree, emphasizing its efficacy in handling the intricate dynamics of polymeric coating drying processes. In conclusion, this study contributes a valuable tool for optimizing polymeric coating processes, reducing production defects, and enhancing overall manufacturing quality and cost-effectiveness.

A hybrid Extreme Learning Machine model with Lévy flight Chaotic Whale Optimization Algorithm for Wind Speed Forecasting

Efficient and accurate prediction of renewable energy sources (RES) is an interminable challenge in efforts to assure the stable and safe operation of any hybrid energy system due to its intermittent nature. High integration of RES especially wind energy into the existing power sector in recent years has made the situation still challenging which draws the attention of many researchers in developing a computationally efficient forecast model for accurately predicting RES. With the advent of Neural network based methods, ELM -Extreme Learning Machine, a typical Single Layer Feedforward Network (SLFFN), has gained a significant attention in recent years in solving various real-time complex problems due to simplified architecture, good generalization capabilities and fast computation. However, since the model parameters are randomly assigned, the conventional ELM is frequently ranked as the second-best model. As a solution, the article attempts to construct a unique optimized Extreme Learning Machine (ELM) based forecast model with improved accuracy for wind speed forecasting. A novel swarm intelligence technique- Lévy flight Chaotic Whale Optimization algorithm (LCWOA) is utilized in the hybrid model to optimize different parameters of ELM. Despite having a appropriate convergence rate, WOA is occasionally unable to discover the global optima due to imbalanced exploration and exploitation when using control parameters with linear variation. An improvement in the convergence rate of WOA can be expected by incorporating chaotic maps in the control parameters of WOA due to their ergodic nature. In addition to this, Lévy flight can significantly improve the intensification and diversification of the Whale Optimization algorithm (WOA) resulting in improvised search ability avoiding local minima. The prediction capability of the suggested hybrid Extreme Learning Machine (ELM) based forecast model is validated with nine other existing models. The experimental study affirms that the suggested model outperform existing forecasting methods in a variety of quantitative metrics.

Irregular convolution strategy based tensorized type-2 single layer feedforward network

Irregular convolution strategy based tensorized type-2 single layer feedforward network

A low-resolution real-time face recognition using extreme learning machine and its variants

ABSTRACT Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of , justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.

Twin Broad Learning System for Fault Diagnosis of Rotating Machinery

As rotating machines are more and more widely used in modem equipment, their fault diagnosis is important to guarantee the instrument’s reliability and safety. Although intelligent fault diagnosis based on deep learning (DL) has achieved great performance in many fault diagnosis tasks, these models are highly dependent on the large-scale training dataset and can be time-consuming during training and testing. These limitations largely affect the efficiency of diagnosis in real-world applications. An effective alternative way is to develop the single-layer feedforward networks (SLFNs) based Broad Learning System (BLS) for fault diagnosis tasks, which enjoy fast training speed as well as strong generalization ability. However, we found that the least square classifier used in classical BLS can have a problem distinguishing the possibly overlapping fault patterns. In this paper, we propose a novel twin broad learning system (TBLS) for fault diagnosis of rotating machinery. Rather than using the classical least square method, the proposed TBLS learns to find two nonparallel hyper-planes to deal with the classification problem, which shows stronger generalization ability in fault diagnosis problems. Experimental results on two fault diagnosis benchmark datasets for typical rotating machinery further illustrate the effectiveness of the TBLS methods, which offer a high-efficiency solution for rotating machinery fault diagnosis.

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation.

As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions between training data and testing data are inconsistent. In this article, we propose a novel ELM called two-stage transfer extreme learning machine (TSTELM) to solve this problem. At the statistical matching stage, we adopt maximum mean discrepancy (MMD) to narrow the distribution difference of the output layer between domains. In addition, at the subspace alignment stage, we align the source and target model parameters, design target cross-domain mean approximation, and add the output weight approximation to further promote the knowledge transferring across domains. Moreover, the prediction of test sample is jointly determined by the ELM parameters generated at the two stages. Finally, we investigate the proposed approach in classification task and conduct experiments on four public domain adaptation datasets. The result indicates that TSTELM could effectively enhance the knowledge transfer ability of ELM with higher accuracy than other existing transfer and non-transfer classifiers.

Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

As a special case of general fuzzy numbers, the polygonal fuzzy number can describe a fuzzy object by means of an ordered representation of finite real numbers. Different from general fuzzy numbers, the polygonal fuzzy numbers overcome the shortcoming of complex operations based on Zadeh’s traditional expansion principle, and can maintain the closeness of arithmetic operation. Hence, it is feasible to use a polygonal fuzzy number to approximate a general fuzzy number. First, an extension theorem of continuous functions on a real compact set is given according to open set construction theorem. Then using Weierstrass approximation theorem and ordered representation of the polygonal fuzzy numbers, the convergence of a single hidden layer feedforward polygonal fuzzy neural network is proved. Secondly, the gradient vector of the approximation error function and the optimization parameter vector of the network are given by using the ordered representation of polygonal fuzzy numbers, and then the gradient descent algorithm is used to train the optimal parameters of the polygonal fuzzy neural network iteratively. Finally, two simulation examples are given to verify the approximation ability of the network. Simulation result shows that the proposed network and the gradient descent algorithm are effective, and the single hidden layer feedforward network have good abilities in learning and generalization.

A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks

This paper aims to propose a framework for manifold regularization (MR) based distributed semi-supervised learning (DSSL) using single layer feed-forward neural network (SLFNN). The proposed framework, denoted as DSSL-SLFNN is based on the SLFNN, MR framework, and distributed optimization strategy. Then, a series of algorithms are derived to solve DSSL problems. In DSSL problems, data consisting of labeled and unlabeled samples are distributed over a communication network, where each node has only access to its own data and can only communicate with its neighbors. In some scenarios, DSSL problems cannot be solved by centralized algorithms. According to the DSSL-SLFNN framework, each node over the communication network exchanges the initial parameters of the SLFNN with the same basis functions for semi-supervised learning (SSL). All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are privacy preserving methods. Finally, several simulations are presented to show the efficiency of the proposed framework and the derived algorithms.

Short-term forecasting of global solar irradiance in tropical environments with incomplete data

Electricity access is a common issue around the world. Many countries that face this problem are located in the tropical region and solar energy might be an alternative to mitigate this limitation. Accurate mechanisms for forecasting solar irradiance and insolation provide important information regarding the potential for generating solar energy. Furthermore, this data is relevant for the planning of renewable energy projects, and energy policy formulation. This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation that only requires solar irradiance historical data for training. Our approach includes a data imputation stage to handle missing data. In the prediction stage, we consider four data-driven approaches: Autoregressive Integrated Moving Average, Single Layer Feed Forward Network, Multiple Layer Feed Forward Network, and Long Short-Term Memory. The experiments are performed in a real-world dataset collected by 12 Automatic Weather Stations located in Nariño - Colombia. Our results show that the neural network-based models outperform Autoregressive Integrated Moving Average in most cases, and that Long Short-Term Memory exhibits better performance in cloudy environments (where more randomness is expected).

Smart Digital Mammographic Screening System for Bulk Image Processing

Treating breast cancer is easier at early stages. However, proper diagnosis is essential for this purpose. Mammography helps in early detection of cancer cells. Existence of masses, calcification and mammogram are the evidences that help radiologists in early cancer identification. This paper proposes a smart digital mammographic screening system for processing images in large volumes irrespective of the nature of images. Watershed segmentation is performed based on appropriate selection of internal and external markers using multiple threshold extended maxima transformations in this technique. Distinguishing between healthy breast tissue and masses can be performed efficiently using a two-stage classifier. Extreme Learning Machine based single layer feed forward network along with Bayesian classifier is used for reducing false positive areas. Feature vector with features like texture and contrast are calculated using these approaches. Digital Mammography Screening database (DMS) is created with 100 mammographic images for the purpose of evaluation. Further, online databases like Breast Cancer Database (BCDB) and BreakHis are also used for analysis. Overall sensitivity of the datasets using the Bayesian classifier and Extreme Learning Machine is found to be 85% and 90% respectively.

Ідентифікація математичної моделі лабораторної установки ланки квадрокоптера

The paper describes a laboratory setup of a quadrocopter link, which is a nonlinear plant. Experimental studies on the control of the movement of the installation were carried out and an array of experimental data was obtained. In order to perform the identification of the mathematical model of the installation, the array was processed. The procedure is consisted of separate stages. At the beginning of the calculations, measurement errors were eliminated. Subsequently, an array of discrete values of the angular velocity of the rod (quadrocopter link) movement is determined. After that, the resulting array was filtered and data was generated in a format suitable for training an artificial neural network. Such data included the pairs: „current value of the voltage drive, current value of the angle, current value of the angular velocity” - „subsequent value of the angle, subsequent value of the angular velocity”. The neural network (predictor) was a single-layer feedforward network with three inputs and two outputs. Artificial neural network has been trained according to the paradigm of supervised training. As a result, a predictor has been obtained that allows predicting the behavior of the plant under a certain control (drive supply voltage). The quality of the predictor's work was estimated based on the analysis of graphical dependencies and in terms of the standard deviations of the experimental (in the case of angular velocity – calculated) and predicted values. It gives grounds to state that the obtained predictor (mathematical model of the plant) may be used in order to synthesize control systems.

GOF-SLFN- An Intelligent Attack Detection System against Denial of Service (DoS) attacks based on Glow Worm Swarm optimized Single Layer Feed Forward Networks for vehicular Cyber Physical Systems (VCPS)

Vehicular cyber-physical network use either wireless communications or internet to communicate with each other for an efficient data transfer. Data includes transportation details, safety, mobility and sustainability. However, these communication networks for vehicular transportation are susceptible to different cyber-attacks, which may lead to severe damages or huge casualties in vehicular infrastructures. Hence the intelligent attack detection system needs to be obtaining the lime light of research based on recent machine and deep learning algorithms. In this paper, the GOF-SLFN (Glowworm optimized features for Single-layer feed-forward network) is proposed to detect the attacks among the vehicular cyber physical systems. Since DoS attacks can happen at any layer of OSI Model such as application, network and transport the foremost purpose of the paper is to detect the DoS attacks using the proposed intelligent algorithm. The glowworm algorithm extracted the optimum features based on the probabilistic mechanism and SLFN has been proposed using glowworm to detect the DoS attacks. The proposed system outperformed in detection when compared with artificial neural network, SLFN, Multi-layer Perceptron and random forest. The proposed GOF-SLFN has been again compared with firefly, Bat and Latent Dirichlet Allocation (LDA) and found the proposed intelligent system provided promising results for detection of DoS attacks in VCPS. The proposed system has been tested on the real time scenarios created with SUMO and OMNET++ environment integrated with CIC IDS 2018 datasets. The performance metrics considered were based on accuracy, sensitivity and specificity.

Robust extreme learning machine for modeling with unknown noise

Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches.

Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.

Smoothing L0 Regularization for Extreme Learning Machine

Extreme learning machine (ELM) has been put forward for single hidden layer feedforward networks. Because of its powerful modeling ability and it needs less human intervention, the ELM algorithm has been used widely in both regression and classification experiments. However, in order to achieve required accuracy, it needs many more hidden nodes than is typically needed by the conventional neural networks. This paper considers a new efficient learning algorithm for ELM with smoothing L0 regularization. A novel algorithm updates weights in the direction along which the overall square error is reduced the most and then this new algorithm can sparse network structure very efficiently. The numerical experiments show that the ELM algorithm with smoothing L0 regularization has less hidden nodes but better generalization performance than original ELM and ELM with L1 regularization algorithms.

Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.

A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of Extreme Learning Machines for classification problems

Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.

Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine

Multiple-instance learning (MIL) can solve supervised learning tasks, where only a bag of multiple instances is labeled, instead of a single instance. It is considerably important to develop effective and efficient MIL algorithms, because real-world datasets usually contain large instances. Known for its good generalization performance, MIL based on extreme learning machines (ELM-MIL) has proven to be more efficient than several typical MIL classification methods. ELM-MIL selects the most qualified instances from each bag through a single hidden layer feedforward network (SLFN) and trains modified ELM models to update the output weights. This learning approach often performs susceptible to the number of hidden nodes and can easily suffer from over-fitting problem. Using Bayesian inferences, this study introduces a Bayesian ELM (BELM)-based MIL algorithm (BELM-MIL) to address MIL classification problems. First, weight self-learning method based on a Bayesian network is applied to determine the weights of instance features. The most qualified instances are then selected from each bag to represent the bag. Second, BELM can improve the classification model via regularization of automatic estimations to reduce possible over-fitting during the calibration process. Experiments and comparisons are conducted with several competing algorithms on Musk datasets, images datasets, and inductive logic programming datasets. Superior classification accuracy and performance are demonstrated by BELM-MIL.

Geometric optimisation of magnetorheological valve using feedforward neural networks for distribution of magnetic flux density inside the valve

Magnetorheological (MR) fluids are very popular among different smart materials. MR valves are used in dampers for different engineering applications where magnetostatic analysis plays a vital role in analysing the performance indices of the MR valve. In the present study, single hidden layered feedforward neural network (FNN) model is proposed to predict the magnetic flux density in the MR valve using magnetic field data generated from a finite element (FE) model in ANSYS-APDL software package. Application of neural networks in magnetostatic analysis has not been reported till now. The proposed FNN has two geometric parameters and applied current as its features (input design variables). The number of activation units in the hidden layer of FNN has been determined through error analysis. After the architecture of the FNN is defined, the FNN is trained to get optimal weights for the features and output of the hidden layer. Predictions made by the FNN and FE models are then evaluated using mean squared error method and it is shown that the FNN model’s results agree with FE result fairly well. The fitted FNN model is then utilised in geometric optimisation of MR valve using a combination of genetic algorithm and sequential quadratic programming method for different application-specific weight factors. Optimal results are then compared to the unoptimized one for improvements in the objective function according to the weight factors allotted.

A New Approach to Cross-Modal Retrieval

The semantic correlation matching algorithm, which attempts to integrate semantic information based on the unified space obtained by CCA learning, achieves an improvement in the effect of cross-modal retrieval. However, there is still a lot of room for the algorithm to make further progress in the generation of shared subspace. This paper proposes a semantic correlation matching algorithm based on the single-layer feed forward network. It generates features through neural networks to represent weight and bias. Meanwhile, it maximizes the correlation between different modalities by means of cross-coupling. The experimental results show that the algorithm proposed in this paper is superior to the compared method in terms of cross-modal retrieval task of image-text and has better cross-modal retrieval ability.