Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

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Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.publishedVersio

A new hybrid cascaded switched-capacitor reduced switch multilevel inverter for renewable sources and domestic loads

This multilevel inverter type summarizes an output voltage of medium voltage based on a series connection of power cells employing standard configurations of low-voltage components. The main problems of cascaded switched-capacitor multilevel inverters (CSCMLIs) are the harmful reverse flowing current of inductive loads, the large number of switches, and the surge current of the capacitors. As the number of switches increases, the reliability of the inverter decreases. To address these issues, a new CSCMLI is proposed using two modules containing asymmetric DC sources to generate 13 levels. The main novelty of the proposed configuration is the reduction of the number of switches while increasing the maximum output voltage. Despite the many similarities, the presented topology differs from similar topologies. Compared to similar structures, the direction of some switches is reversed, leading to a change in the direction of current flow. By incorporating the lowest number of semiconductors, it was demonstrated that the proposed inverter has the lowest cost function among similar inverters. The role of switched-capacitor inrush current in the selection of switch, diode, and DC source for inverter operation in medium and high voltage applications is presented. The inverter performance to supply the inductive loads is clarified. Comparison of the simulation and experimental results validates the effectiveness of the proposed inverter topology, showing promising potentials in photovoltaic, buildings, and domestic applications. A video demonstrating the experimental test, and all manufacturing data are attached. © 2013 IEEE

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients

Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc ), critical pressure (Pc ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries

Robust adaptive synchronization of a class of uncertain chaotic systems with unknown time-delay

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems

Social capital contributions to food security: A comprehensive literature review

Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state of the art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families, and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members’ resilience and decreases the community’s vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study’s findings to provide solutions to address food insecurity problems

Recurrent neural network and reinforcement learning model for COVID-19 prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate

A fault-tolerant cascaded switched-capacitor multilevel inverter for domestic applications in smart grids

Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached. © 2013 IEEE

Time series-based groundwater level forecasting using gated recurrent unit deep neural networks

In this research, the mean monthly groundwater level with a range of 3.78 m in Qoşaçay plain, Iran, is forecast. Regarding three different layers of gated recurrent unit (GRU) structures and a hybrid of variational mode decomposition with gated recurrent unit (VMD-GRU), deep learning-based neural network models are developed. As the base model for performance comparison, the general single-long short-term memory-layer network model is developed. In all models, the module of sequence-to-one is used because of the lack of meteorological variables recorded in the study area. For modeling, 216 monthly datasets of the mean monthly water table depth of 33 different monitoring piezometers in the period April 2002–March 2020 are utilized. To boost the performance of the models and reduce the overfitting problem, an algorithm tuning process using different types of hyperparameter accompanied by a trial-and-error procedure is applied. Based on performance evaluation metrics, the total learnable parameters value and especially the model grading process, the new double-GRU model coupled with multiplication layer (×) (GRU2× model) is chosen as the best model. Under the optimal hyperparameters, the GRU2× model results in an R 2 of 0.86, a root mean square error (RMSE) of 0.18 m, a corrected Akaike’s information criterion (AICc) of −280.75, a running time for model training of 87 s and a total grade (TG) of 6.21 in the validation stage; and the hybrid VMD-GRU model yields an RMSE of 0.16 m, an R 2 of 0.92, an AICc of −310.52, a running time of 185 s and a TG of 3.34. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group