Mixture extreme learning machine algorithm for robust regression

The extreme learning machine (ELM) is a well-known approach for training single hidden layer feedforward neural networks (SLFNs) in machine learning. However, ELM is most effective when used for regression on datasets with simple Gaussian distributed error because it often employs a squared loss in its objective function. In contrast, real-world data is often collected from unpredictable and diverse contexts, which may contain complex noise that cannot be characterized by a single distribution. To address this challenge, we propose a robust mixture ELM algorithm, called Mixture-ELM, that enhances modeling capability and resilience to both Gaussian and non-Gaussian noise. The Mixture-ELM algorithm uses an adjusted objective function that blends Gaussian and Laplacian distributions to approximate any continuous distribution and match the noise. The Gaussian mixture accurately models the residual distribution, while the inclusion of the Laplacian distribution addresses the limitations of the Gaussian distribution in identifying outliers. We derive a solution to the novel objective function using the expectation maximization (EM) and iteratively reweighted least squares (IRLS) algorithms. We evaluate the effectiveness of the algorithm through numerical simulation and experiments on benchmark datasets, thereby demonstrating its superiority over other state-of-the-art machine learning methods in terms of robustness and generalization

QL-ADIFA: Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm

Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Although FA has been significantly enhanced to improve its performance, it still exhibits certain deficiencies. To overcome these limitations, this study presents the Q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (QL-ADIFA). The Q-learning technique empowers the improved firefly algorithm to leverage the firefly's environmental awareness and memory while in flight, allowing further refinement of the enhanced firefly. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on 15 benchmark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020

Solution to Singular Optimal Control by Backward Differential Flow

This paper presents a backward differential flow for solving singular optimal control problems. By using Krotov equivalent transformation, the cost functional is converted to a class of global optimization problems. Some properties of the flow are given to reveal the significant relationship between the dynamic of the flow and the geometry of the feasible set. The proposed method is also used in solving a class of variational problems. Some examples are illustrated

Solution to Singular Optimal Control by Backward Differential Flow

This paper presents a backward differential flow for solving singular optimal control problems. By using Krotov equivalent transformation, the cost functional is converted to a class of global optimization problems. Some properties of the flow are given to reveal the significant relationship between the dynamic of the flow and the geometry of the feasible set. The proposed method is also used in solving a class of variational problems. Some examples are illustrated

An integrated federated learning algorithm for short-term load forecasting

Accurate power load forecasting plays an integral role in power systems. To achieve high prediction accuracy, models need to extract effective features from raw data, and the training of models needs a large amount of data. However, data sharing will require the disclosure of the private data of the participants. To address this issue, we combined variational mode decomposition (VMD), the federated k-means clustering algorithm (FK), and SecureBoost into a single algorithm, called VMD-FK-SecureBoost. First, we used VMD to decompose the original data into several sub-sequences. This enabled us to extract the implied features to separately predict each sub-sequence to improve the prediction accuracy. Second, we use FK to recombine the sub-sequences into several clusters with common characteristics. Finally, with SecureBoost, we use clustering results to realize federated learning with privacy protection. We calculated the prediction values by accumulating the prediction results of the sub-sequences. The results for the examples in the US and Australia showed that the prediction performance of VMD-FK-SecureBoost was better than those of XGBoost and SecureBoost. Particularly, the MAPEs of one-step-ahead forecasting in the Texas and Newcastle CBD from our proposed method are 0.209% and 2.127% respectively, which are the lowest of all the algorithms

A Meaningful Strategy for Glioma Diagnosis via Independent Determination of hsa_circ_0004214

Glioma is one of the most common primary tumors in the central nervous system. Circular RNAs (circRNAs) may serve as novel biomarkers of various cancers. The purpose of this study is to reveal the diagnostic value of hsa_circ_0004214 for glioma and to predict its molecular interaction network. The expression of hsa_circ_0004214 was evaluated by RT-qPCR. The vector and siRNAs changed the expression of hsa_circ_0004214 to judge its influence on the migration degree of glioma cells. hsa_circ_0004214 can be stably expressed at a high level in high-grade glioma tissue (WHO III/IV). The area under the ROC curve of hsa_circ_0000745 in glioma tissue was 0.88, suggesting good diagnostic value. While used to distinguish high-grade glioma, AUC value can be increased to 0.931. The multi-factor correlation analysis found that the expression of hsa_circ_0004214 was correlated with GFAP (+) and Ki67 (+) in immunohistochemistry. In addition, the migration capacity of U87 was enhanced by overexpression of hsa_circ_0004214. Through miRNA microarray analysis and database screening, we finally identified 4 miRNAs and 9 RBPs that were most likely to interact with hsa_circ_0004214 and regulate the biological functions of glioma. Hsa_circ_0004 214 plays an important role in glioma, its expression level is a promising diagnostic marker for this malignancy

An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting

Load forecasting can effectively reduce the operating costs of the power industry, while attacks on the load can lead to inaccurate forecasts. In the existing reports, the robust regression method can potentially alleviate the interference of the attack for load forecasting. However, most current existing methods can handle the data under symmetric attacks, which are not effective in data under asymmetric attacks. In this paper, an asymmetric robust regression method (asymmetric bisquare regression) is proposed for mixed cyberattack-resilient load forecasting. Instead of the symmetric bisquare loss function, in the asymmetric bisquare loss function, two tuning parameters are introduced to control the impacts from negative and positive attacks, respectively. Particularly, the two tuning parameters can be adaptively estimated according to the proportion of attack type. Finally, we demonstrate that the asymmetric robust regression method is superior to all considered robust statistical regression methods through a comparative study of mixed cyberattack-resilient load forecasting.</p

An effective dimensionality reduction approach for short-term load forecasting

Accurate power load forecasting has a significant effect on a smart grid by ensuring effective supply and dispatching of power. However, electric load data generally possesses the characteristics of nonlinearity, periodicity, and seasonality. For complex electric load systems, the presence of redundant information potentially reduces the real pattern extraction for load forecasting. Bearing in mind these issues, we propose an effective forecasting model in which a feature extraction module is introduced that is combined with the variational mode decomposition (VMD) with the variational autoencoder (VAE). In this combination, VMD is utilized for decomposing complex load series and VAE is used to filter the redundant information (noises) from each decomposed series. With two real data sets from China, we demonstrate that the proposed model can achieve highly accurate predictions, as we find the mean absolute percentage error (MAPE) values for one-step-ahead prediction to be 1% (Nanjing) and 0.8% (Taixing), respectively

QQLMPA: A quasi-opposition learning and Q-learning based marine predators algorithm

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters