Latest Research Articles on Math

In this paper, we provide an overview of type-3 fuzzy logic systems (T3FLSs) and their applications in a general way. The contribution of this paper is to analyze and review, in the best way possible, applications in several fields utilizing type-3 fuzzy logic systems. Recently, many algorithms are receiving more and more attention in this area, and for this reason, an overview of this field is important. This article provides an overview of the most important applications in which intelligent computing methods based on T3FLSs are used. The main goal of this paper is to thoroughly explore […]

The global adoption of hybrid renewable energy systems (HRESs) is accelerating as a strategic response to escalating energy demands and the imperative to mitigate greenhouse gas emissions. Despite the development of various technological tools, such as pre-feasibility analysis, sizing, and simulation tools, challenges persist due to their limited flexibility in modifying system architectures and their typically long computation times, which hinder their practical efficiency. This study introduces a novel hybrid method that integrates the Branch and Bound (BB) heuristic search algorithm with […]

This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly […]

This article uses an approach based on the triad model–algorithm–program. The model is a nonlinear dynamic Selkov system with non-constant coefficients and fractional derivatives of the Gerasimov–Caputo type. The Adams–Bashforth–Multon numerical method from the predictor–corrector family of methods is selected as an algorithm for studying this system. The ABMSelkovFracSim 1.0 software package acts as a program, in which a numerical algorithm with the ability to visualize the research results is implemented to build oscillograms and […]

Upper-air wind fields play a crucial role in aircraft navigation, directly impacting flight safety and operational efficiency. In this study, we propose an advanced route planning framework that integrates wind field predictions derived from a neural network-based approach. Specifically, we leverage the PredRNN Sequence-to-Sequence algorithm to predict wind fields up to 10 hours in advance. The model is trained on grid-based wind speed data at an altitude of approximately 5500 m, focusing on major airline routes over China. Our approach demonstrates superior accuracy in wind field forecasting […]

This study introduces a fuzzy interval-valued approach using multi-objective linear programming to optimize passenger–driver matching in ride-hailing systems, addressing uncertainties in waiting times and fare preferences. The model aims to minimize total waiting times, balance job allocations among drivers, and reduce deviations in fare expectations between passengers and drivers. The proposed framework effectively manages operational uncertainties by incorporating interval-valued fuzzy parameters, including traffic variability and fluctuating demand patterns. Numerical experiments […]

In this paper, we investigate the exterior problem of parabolic Hessian quotient equations. By utilizing Perron’s method, we establish the existence of viscosity solutions that exhibit generalized asymptotic behavior at infinity. The main approach we adopt involves constructing sub- and supersolutions to handle the non-constant term on the right-hand side of the equation.

The question of whether microeconomic shocks induce aggregate fluctuations constitutes a central issue in economic research. This paper introduces a general equilibrium model with production networks to explore the propagation mechanisms of microeconomic shocks. A novel triangular production network structure is introduced, and simulations are performed using China’s input-output table to analyze the propagation of these shocks within the Chinese economy. The model demonstrates that the first-order effects of microeconomic shocks propagate downstream along the industrial chain, […]

Classifying imbalanced data is a well-known challenge in machine learning. One of the fields inherently affected by imbalanced data is credit datasets in finance. In this study, to address this challenge, we employed one of the most recent methods developed for classifying imbalanced data, CorrOV-CSEn. In addition to the original CorrOV-CSEn approach, which uses AdaBoost as its base learning method, we also applied Multi-Layer Perceptron (MLP), random forest, gradient boosted trees, XGBoost, and CatBoost. Our dataset, sourced from the Iran capital market from 2015 to 2022, utilizes the more […]

The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism […]

This paper formalizes the analytic expressions and some properties of the evolution operator that generates the state-trajectory of dynamical systems combining delay-free dynamics with a set of discrete, or point, constant (and not necessarily commensurate) delays, where the parameterizations of both the delay-free and the delayed parts can undergo impulsive changes. Also, particular evolution operators are defined explicitly for the non-impulsive and impulsive time-varying delay-free case, and also for the case of impulsive delayed time-varying systems. In the impulsive cases, in general, […]

Modeling distributed parameter systems (DPSs) is challenging due to their strong nonlinearity and spatiotemporal coupling. In this study, a three-dimensional fuzzy modeling method combining genetic algorithm (GA)-based automatic clustering and hierarchical extreme learning machine (HELM) is proposed for DPS modeling. The method utilizes GA-based automatic clustering to learn the premise part of 3D fuzzy rules, while HELM is employed to learn spatial basis functions and construct a complete fuzzy rule base. This approach effectively captures the spatiotemporal coupling characteristics of the […]