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In this paper, we improve the broad learning system (BLS) by speeding up the incremental learning for added inputs. We propose an efficient implementation for a step that is in the pseudoinverse computation of a partitioned matrix, to reduce the computational complexity. The proposed efficient implementation has two different forms for the cases of q > k and q ≤ k , respectively, where q and k denote the number of additional training samples and the total number of nodes, respectively. The proposed implementation for q > k utilizes the inverse of a sum of matrices to compute only a k × k matrix inverse, instead of a q × q matrix inverse in the original implementation, and the corresponding speedup for the matrix inversion operation in the number of floating-point operations is 1 2 (q / k) 3 . Moreover, it also speeds up two relevant matrix multiplication operations in the original implementation. On the other hand, the proposed implementation for q ≤ k speeds up one matrix multiplication operation in the original implementation. The numerical simulations show that both the proposed and original implementations always achieve the same testing accuracy. On the Modified National Institute of Standards and Technology dataset, the speedups of the proposed efficient BLS implementation over the original BLS implementation in total training time are 1.35–1.57 when q > k and 1.09–1.13 when q < k , while on the NYU object recognition benchmark dataset, the speedups are 1.10–1.35 when q > k and 1.07–1.09 when q < k. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability. [ABSTRACT FROM AUTHOR]

During subway tunnel construction, the consumed time for each ring (unit of construction progress) is highly dependent on objective factors such as geological conditions and shield performances, as well as subjective reasons including workers' proficiency and contractors' management skills. It is nontrivial to score each ring's efficiency from an objective angle, so that workers and contractors can receive fair evaluations. In this paper, a scoring method driven by construction big data is proposed. First, to resolve the high-dimensionality problem, a Gaussian mixture model (GMM) was employed to cluster rings of similar conditions in a probabilistic style so that detailed information can be retained. Second, the standard time to finish one ring was analyzed, so that each ring can be labeled as fail or pass, and our task can be considered as a classification problem. Third, for each cluster, a broad learning system (BLS) was developed as a classifier due to its advantages of fast computation and incremental learning. Finally, the BLS was trained with real tunneling data of 23,822 rings, and then scorecards were developed, where results of validation and statistical tests suggested that our method outperforms conventional ones. Feedback from the subway company and two compared contractors suggested that the proposed method is fair and practical, and it could reveal management problems that were easily overlooked. For subway shield tunnels, this study proposes a construction progress scoring method driven by big data for subway companies to evaluate the performance of each construction unit, e.g., each ring of the shield tunnel is treated as a sample analysis. First, in view of the difficulty of many geological types and risk variables, the Gaussian mixture method is used to classify the samples and obtain probability that each ring belongs to a certain geological type. Statistical analysis is used to calculate the shield standard time of each geological type, so that the time interval of pass can be determined. Second, a broad learning model with low calculation and updating cost is constructed to fit the mapping relationship between geological factors and construction progress. With this model, each ring can be scored. Finally, through the model training and verification of 23,822 ring data in seven subway intervals, results suggest that the method is practical. Our method can objectively reflect the work efficiency and management ability of different contractors and workers. In addition, suggestions can be elicited to improve the management level of construction units. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis (IFD) plays an indispensable role in protecting machinery from catastrophic accidents. Existing IFD methods are mainly developed in the framework of one-time learning. Therefore, they work under the hypothesis of complete dataset. Nevertheless, it is unrealistic to gain the complete dataset of machinery faults at once. More practically, new data will be progressively acquired over time. Therefore, it is urgently required to develop the incremental learning (IL) capabilities for IFD models to learn new knowledge continually from new data. For this purpose, this study proposes an improved broad learning system (IBLS) for lifelong learning IFD. Firstly, the initial IBLS is constructed based on the original broad learning system (BLS). Then, the IL capabilities of the IBLS are developed for three scenarios: increasing fault samples, increasing fault modes, and increasing running conditions. Based on these IL capabilities, the IBLS can be progressively updated to learn more and more diagnosis functions. Finally, the effectiveness of the proposed IBLS is verified using three experiments of high-speed train bearing, disc component, and Case Western Reserve University bearing. The results show that the IBLS is capable of learning continually new knowledge from new data. Besides, the diagnosis accuracy of the IBLS is 12.45%, 7.84%, and 5.10% higher than that of the original BLS in the three case studies. The satisfying results prove that the proposed IBLS is a useful method to solve the lifelong learning IFD problem. • An improved broad learning system (IBLS) is developed. • An IBLS based lifelong learning intelligent diagnosis framework is proposed. • Three case studies are carried out for validations. [ABSTRACT FROM AUTHOR]

This article investigates the feasibility of applying the broad learning system (BLS) to realize a novel Takagi–Sugeno–Kang (TSK) neuro-fuzzy model, namely a broad learning based dynamic fuzzy inference system (BL-DFIS). It not only improves the accuracy and interpretability of neuro-fuzzy models but also solves the challenging problem that models are incapable of determining the optimal architecture autonomously. BL-DFIS first accomplishes a TSK fuzzy system under the framework of BLS, in which an extreme learning machine auto-encoder is employed to obtain feature representation in a fast and analytical way, and an interpretable linguistic fuzzy rule is integrated into the enhancement node to ensure the high interpretability of the system. Meanwhile, the extended-enhancement unit is designed to achieve the first-order TSK fuzzy system. In addition, a dynamic incremental learning algorithm with internal pruning and updating mechanism is developed for the learning of BL-DFIS, which enables the system to automatically assemble the optimal structure to obtain a compact rule base and an excellent classification performance. Experiments on benchmark datasets demonstrate that the proposed BL-DFIS can achieve a better classification performance than some state-of-the-art nonfuzzy and neuro-fuzzy methods, simultaneously using the most parsimonious model structure. [ABSTRACT FROM AUTHOR]

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training. [ABSTRACT FROM AUTHOR]

The broad learning system (BLS) is designed based on the technology of compressed sensing and pseudo-inverse theory, and consists of feature nodes and enhancement nodes, has been proposed recently. Compared with the popular deep learning structures, such as deep neural networks, BLS has the ability of rapid incremental learning and can remodel the system without the usual tedious retraining process. However, given that BLS is still in its infancy, it still needs analysis, improvements, and verification. In this article, we first analyze the principle of fast incremental learning ability of BLS in depth. Second, in order to provide an in-depth analysis of the BLS structure, according to the novel structure design concept of deep neural networks, we present four brand-new BLS variant networks and their incremental realizations. Third, based on our analysis of the effect of feature nodes and enhancement nodes, a new BLS structure with a semantic feature extraction layer has been proposed, which is called SFEBLS. The experimental results show that SFEBLS and its variants can increase the accuracy rate on the NORB dataset 6.18%, Fashion-MNIST dataset by 3.15%, ORL data by 5.00%, street view house number dataset by 12.88%, and CIFAR-10 dataset by 18.42%, respectively, and the four brand-new BLS variant networks also obviously outperform the original BLS. [ABSTRACT FROM AUTHOR]

Broad Learning System (BLS) has been proven to be one of the most important techniques for classification and regression in machine learning and data mining. BLS directly collects all the features from feature and enhancement nodes as input of the output layer, which neglects vast amounts of redundant information. It usually leads to be inefficient and overfitting. To resolve this issue, we propose sparse regularization-based compact broad learning system (CBLS) framework, which can simultaneously remove redundant nodes and weights. To be more specific, we use group sparse regularization based on ℓ 2 , 1 norm to promote the competition between different nodes and then remove redundant nodes, and a class of nonconvex sparsity regularization to promote the competition between different weights and then remove redundant weights. To optimize the resulting problem of the proposed CBLS, we exploit an efficient alternative optimization algorithm based on proximal gradient method together with computational complexity. Finally, extensive experiments on the classification task are conducted on public benchmark datasets to verify the effectiveness and superiority of the proposed CBLS. [ABSTRACT FROM AUTHOR]

Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article extends the broad learning system (BLS) to a framework for constructing robust representations in low-dimensional and small data sets. First, the BLS changed from a supervised prediction method to an ensemble feature extraction method. Second, feature extraction methods instead of random mapping are used to generate mapped features. Third, deep representations, called enhancement features, are learned from the ensemble mapped features. Fourth, data for generating mapped features and enhancement features can be randomly selected. The ensemble of mapped features and enhancement features can provide robust representations to enhance the performance of downstream tasks. A label-based autoencoder (LA) is embedded in the BLS framework as an example to show the effectiveness of the framework. A random LA (RLA) is presented to generate more different features. The experimental results show that the BLS framework can construct robust representations and significantly promote the performance of machine learning models. [ABSTRACT FROM AUTHOR]

Since it is difficult to online measure the underflow concentration of the thickener in the thickening-dehydration process, a broad learning system(BLS) based soft sensor modeling method is proposed in this paper. The method has high precision and strong generalization capability. First, several pressure sensors are installed inside the thickener, and the historical dataset under normal operating conditions is established. Then, the soft sensor model is trained by employing the BLS method to online predict the underflow concentration of the thickener. Finally, the efficiency of the proposed method is verified by simulation experiments. Compared with other traditional machine learning methods, the BLS method has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for the construction of a transient stability boundary (TSB) that overcomes the lack of TSB analytical description for multiple operating states and contingencies in power systems. The proposed approach models the TSB as a nonlinear mapping between the system parameters of interest and the corresponding critical clearing time (CCT). This compact mathematical expression is built based on an efficient discriminative network, broad learning system (BLS). While keeping a high accuracy and generalization ability, the newly introduced construction method provides an efficient update strategy that does not require an entire retraining cycle. These properties make this method suitable for online transient stability assessment (TSA). The prospects of the developed method are verified via case studies by employing the IEEE 9-bus, IEEE 39-bus (New England) test systems and a real system of China Southern Power Grid. In comparison with existing methods, the newly introduced algorithm has a higher prediction accuracy and improved robustness. The demonstrated characteristics indicate that the proposed method may serve as a valuable tool in the framework of online TSA. [ABSTRACT FROM AUTHOR]

Recently, a broad learning system (BLS) has been theoretically and experimentally confirmed to be an efficient incremental learning system. To get rid of deep architecture, BLS shares the same architecture and learning mechanism of the well-known functional link neural networks (FLNN), but works in broad learning way on both the randomly mapped features of original features of data and their randomly generated enhancement nodes. As such, BLS often requires a huge heap of hidden nodes to achieve the prescribed or satisfactory performance, which may inevitably cause both overwhelming storage requirement and overfitting phenomenon. In this study, a stacked architecture of broad learning systems called D&BLS is proposed to achieve enhanced performance and simultaneously downsize the system architecture. By boosting the residuals between previous and current layers and simultaneously augmenting the original input space with the outputs of the previous layer as the inputs of current layer, D&BLS stacks several lightweight BLS sub-systems to guarantee stronger feature representation capability and better classification/regression performance. Three fast incremental learning algorithms of D&BLS are also developed, without the need for the whole re-training. Experimental results on some popular datasets demonstrate the effectiveness of D&BLS in the sense of both enhanced performance and reduced system architecture. [ABSTRACT FROM AUTHOR]

As an efficient learning model, the broad learning system (BLS) has achieved great success in machine learning and various applications due to its outstanding performance. Differing from typical deep learning based models, the BLS has a more straightforward structure and few parameters. In this study, we focus on developing a decentralized version of the elastic-net BLS (D-ENBLS) with communication efficiency. The scenario we considered is that the training data is distributed throughout a network of interconnected agents, which prohibits sharing the raw data owing to resource limitations or privacy concerns. In such a distributed paradigm, the communication between agents is an important issue to investigate. From the perspective of saving communication resources, we introduce quantization and communication censoring strategies on the D-ENBLS algorithm to improve the communication procedure and minimize the communication cost with minimal performance degradation. The novel algorithm refers to as DQC-ENBLS, in which quantization reduces the number of bits for each transmission, whereas communication censoring reduces the total number of transmissions. By formulating the training problem as a finite-sum minimization, the alternating multiplication method (ADMM) is employed to solve the optimization problem in a decentralized manner. The experimental results verify that the DQC-ENBLS algorithm can reduce communication cost while maintaining similar performance on the test datasets. • A decentralized learning algorithm for elastic-net regularized BLS is developed. • The quantization and communication censoring strategies are introduced. • The performance of the proposed algorithm is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

• A new Functional link broad learning system is used for solar irradiance prediction. • Large number of mapped feature nodes is created from expanded cluster of inputs. • Random weights and biases and suitable activation functions are used for these nodes. • Mapped feature nodes are used as inputs to enhancement nodes for FLBLS. • A new hybrid optimization technique is used for optimization of FLBLS parameters. A new machine learning approach for forecasting short-termsolar irradiance is presented in this paper considering different weather conditions and time horizon in a microgrid environment. Using historical solar irradiance, a short-term forecasting model known as Robust optimized functional link broad learning system (FLBLS) is developed. Here the original input comprising delayed historical solar irradiance samples is initially expanded by exponential trigonometric functions and the expanded features are then used to create a large number of feature nodes using random weights and biases. Further the newly created features are fed as inputs to the enhancement nodes forming a broad learning system (BLS). This newly formed flat FLBLS facilitates incremental learning in fast remodelling of the network if needed for efficient performance without a retraining process unlike deep neural networks using several hidden layers and filters, etc. The proposed approach is designed on the basis of the standard random vector functional link network (RVFLN), however unlike the traditional RVFLN that applies inputs directly and set up the enhancement nodes, in our proposed approach, initially the inputs are expanded and map the expanded inputs to generate a set of mapped features. It reduces the lengthy process unlike deep neural networks that involves the back propagation algorithm for error minimization. The proposed FLBLS approach simplifies the learning process with the proper choice of feature nodes as it can be implemented to a flat network or a simpler network such as ELM that requires only the simple calculation of the output weight and obtain the final output where retraining is not required for adding extra feature nodes or enhancement nodes like deep neural architectures. Also, the minimum variance strategy with the training samples is useful in rejecting the effects of noise and outliers in the system and make the network robust which gets reflected in computing output weights using generalized least square. The minimum variance strategy further enhances the performance of the proposed system. To improve the prediction accuracy, the FLBLS parameters are optimized using an efficient metaheuristic chaotic firefly-sine cosine levy flight algorithm. Also, to reject the effects of noise and outliers and make the network robust a minimum variance strategy is used with the training samples that gets reflected in computing output weights using generalised least squares. The suggested method is experimented rigorously and analysed with different time frames designed for 30 min and 60 min ahead solar irradiance prediction. The obtained results from experiments confirm that the proposed FLBLS approach outperforms other randomized neural network forecasting methods as well many state-of-the-art methods using solar irradiance data of Hyderabad, India collected from National Renewable Energy Laboratory (NREL). This approach exhibits with the lowest errors in different case studies among all the compared methods. This FLBLS designed approach is an efficient approach for the purpose of solar irradiance prediction with highly nonlinear data. [ABSTRACT FROM AUTHOR]

• An active broad learning system was proposed for ECG arrhythmia classification. • The sigmoid function was used to map the outputs in BLS into posterior probabilities. • ABLS has superior classification performance. • The ABLS can reduce the beats that need to be labeled and time-consumption. • The ABLS only needs to incrementally train the selected beats. This paper presents an active and incremental learning system called active broad learning system (ABLS) for ECG arrhythmia classification to reduce the time-consumption of training and labor cost of experts labeling beats. An effective strategy is designed to convert the actual outputs in broad learning system (BLS) into approximated posterior probabilities for active learning to select the most valuable beats from unlabeled beats. The proposed ABLS is first pre-trained with a small number of labeled beats and then incremental trained with the selected beats labeled by the expert to fine-tune the connection weight. Due to the structural characteristics, ABLS does not need to retrain all the beats, which can greatly reduce the time-consumption. The experimental results on the MIT-BIH arrhythmia database show ABLS can greatly reduce the number of beats that need to be labeled and consume very little training time while maintaining excellent performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]