Research Article
Control Theory & Systems
Mohammad Zangouei; Naser Pariz; Reihaneh Kardehi Moghaddam
Abstract
In this paper, we present an event-triggered fault-tolerant control framework for nonlinear affine multi-agent systems, together with a state-observer–based fault detection scheme. The proposed approach integrates an event-triggered controller that reduces communication and computation while guaranteeing ...
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In this paper, we present an event-triggered fault-tolerant control framework for nonlinear affine multi-agent systems, together with a state-observer–based fault detection scheme. The proposed approach integrates an event-triggered controller that reduces communication and computation while guaranteeing closed-loop stability, with a robust fault-detection mechanism capable of identifying sensor faults, including current-sensor faults, under bus and load disturbances, and leveraging sensor redundancy to enable rapid recovery. A rigorous stability and robustness assessment based on eigenvalue analysis of the observer matrix is complemented by extensive MATLAB simulations that demonstrate resilience to parameter variations and external disturbances. Open-loop analyses under unconventional inputs reveal high sensitivity to fault types while exhibiting insensitivity to load disturbances, underscoring the detector’s discriminative capability. To mitigate startup and transient effects, a low-pass filter is implemented at the detector output, reducing transients and improving fault-detection accuracy for real-time identification of current sensor faults. The overall results show reliable fault detection, rapid recovery, and maintained performance in the presence of sensor faults and load disturbances, thereby enhancing the robustness of nonlinear affine multi-agent systems.
Research Article
Mohammad Zahaby; Mostafa Boroumandzadeh; Iman Makhdoom
Abstract
Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians ...
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Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians and healthcare providers in the management of critical illnesses. This paper presents a Medical Decision Support System (MDSS) that utilizes Yager’s rule of combination to classify and diagnose breast cancer patients by integrating information from multiple data sources. Medical text reports (MTR) and key feature vectors extracted from electronic health records (EHR) were reduced using Principal Component Analysis (PCA) and then classified using Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and Support Vector Machines (SVM). Medical images were preprocessed and classified using a U-Net model. A novel decision fusion algorithm, called weighted Yager, was introduced to determine the Breast Imaging-Reporting and Data System (BI-RADS) categories, taking into account the accuracy of each class in each classifier as evidence. The performance of the proposed system was evaluated based on standard metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. The proposed system achieved the highest accuracy of 96.23\%, outperforming individual classifiers (CNN: 86.37%, MLP: 92.11%, SVM: 87.92%, U-Net: 92.97%, and Yager: 93.49%). The weighted Yager fusion method yielded the best performance with an accuracy of 96.23%, sensitivity of 98.80%, specificity of 85.90%, PPV of 86.21%, NPV of 97.82%, and F1-score of 85.87%. These findings demonstrate that integrating decisions from multiple classifiers significantly improves diagnostic accuracy and robustness.
Research Article
Applied & Interdisciplinary
Ahmad Jalili; Fatemeh Babakordi
Abstract
Energy constraint is the most critical challenge in Wireless Sensor Networks (WSNs), particularly in dynamic environments with mobile nodes. This paper proposes an intelligent clustering protocol based on Fuzzy Neural Networks (FNN) that adaptively optimizes energy consumption by ...
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Energy constraint is the most critical challenge in Wireless Sensor Networks (WSNs), particularly in dynamic environments with mobile nodes. This paper proposes an intelligent clustering protocol based on Fuzzy Neural Networks (FNN) that adaptively optimizes energy consumption by dynamically selecting cluster heads and determining optimal cluster configurations. The FNN integrates fuzzy logic's uncertainty handling with neural networks' learning capabilities, using key parameters including residual energy, node distance, neighbor density, and signal-to-noise ratio. Unlike static clustering approaches such as LEACH and HEED, our method continuously adapts to changing network conditions through real-time parameter evaluation. Extensive MATLAB simulations with 100 nodes demonstrate significant performance improvements: the proposed FNN extends network lifetime by 35% compared to LEACH, 28% compared to HEED, and 15% compared to ANN-based ELDC. The First Node Dies (FND) is delayed by 45%, 38%, and 22% respectively, while achieving 25% lower energy consumption. Results confirm the FNN approach's superior energy efficiency and network stability, making it highly suitable for dynamic WSN applications.
Research Article
Optimization & Operations Research
Jafar Pourmahmoud; Ahad Abbasi; Alireza Ghaffari-Hadigheh
Abstract
Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units. In complex systems comprising multiple interconnected subsections, Network DEA provides a structured framework for efficiency evaluation. However, ...
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Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units. In complex systems comprising multiple interconnected subsections, Network DEA provides a structured framework for efficiency evaluation. However, traditional DEA models rely on the assumption of deterministic data, which inadequately reflects the inherent uncertainty present in real-world scenarios. Traditional uncertainty-handling methods, such as fuzzy logic, stochastic models, and interval-based techniques, often fail when there is limited historical data and when expert opinions significantly influence the dataset. To address these limitations, this study introduces an uncertain network DEA model based on Liu’s uncertainty theory, facilitating a more accurate assessment of efficiency under conditions of data imprecision. The proposed model is designed for three interconnected subsections and is further extended into a generalized multi-stage framework, allowing it to adapt to increasingly complex systems. Its effectiveness and practical applicability are demonstrated through two numerical case studies in the banking industry, highlighting its capacity to support decision-making under uncertainty. The findings emphasize the model's potential to enhance efficiency evaluation methods, particularly in environments characterized by limited and uncertain data.
Research Article
Optimization & Operations Research
Mahdi Dehghani Darmian
Abstract
This paper analyzes systems of linear first-order ordinary differential equations (ODEs) with parametric coefficients, a class of problems that arises in control theory, optimization, and applied mathematics. We introduce the notion of a comprehensive solution ...
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This paper analyzes systems of linear first-order ordinary differential equations (ODEs) with parametric coefficients, a class of problems that arises in control theory, optimization, and applied mathematics. We introduce the notion of a comprehensive solution system for such parametric ODEs, constructed using Gröbner systems from computer algebra. Our approach partitions the parameter space into finitely many cells and associates an explicit solution with each cell. Furthermore, we present an algorithm that computes a comprehensive solution system for any given parametric system. To address the computational challenges inherent in Gröbner systems, we adopt the GES algorithm, a parametric variant of Gaussian elimination, which eliminates the need for Gröbner bases. This method builds upon the LDS algorithm proposed in 2017. Both algorithms have been implemented in Maple, and we illustrate the structural framework of the main algorithm with a straightforward example. The results highlight the practicality and effectiveness of the proposed methods for solving parametric linear first-order ODE systems.
Research Article
Control Theory & Systems
Salam Mcheik; Elyas Shivanian; Youssef El Seblani
Abstract
In this paper, we investigate the existence and uniqueness of solutions for a high-order boundary value problem involving non-integer derivatives, specifically utilizing the Caputo fractional derivative. The problem is subject to non-local boundary conditions. To ...
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In this paper, we investigate the existence and uniqueness of solutions for a high-order boundary value problem involving non-integer derivatives, specifically utilizing the Caputo fractional derivative. The problem is subject to non-local boundary conditions. To tackle this, we introduce the fractional Green's function as an analytical tool. The Banach contraction fixed-point theorem serves as the fundamental method to establish our main results. To support the theoretical findings, we provide illustrative examples. Furthermore, we develop a numerical semi-analytical approach to approximate the unique solution with the desired accuracy.
Research Article
Control Theory & Systems
Ali Dehghani Filabadi; Hossein Nahid Titkanlue
Abstract
Addressing complex decision-making scenarios, particularly those involving multiple criteria and expert perspectives, often requires robust frameworks capable of managing uncertainty and qualitative assessments. The Qualitative Absolute Order-of-Magnitude (QAOM) model offers ...
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Addressing complex decision-making scenarios, particularly those involving multiple criteria and expert perspectives, often requires robust frameworks capable of managing uncertainty and qualitative assessments. The Qualitative Absolute Order-of-Magnitude (QAOM) model offers a flexible approach for expressing subjective evaluations through linguistic terms with adjustable levels of detail. However, practical challenges remain in applying QAOM, including the absence of an inherent system for deriving attribute weights, limitations in coherently synthesizing the judgments from multiple experts, and the lack of systematic normalization procedures for negatively oriented attributes. To address these issues, this paper proposes an advanced multi-attribute group decision-making (MAGDM) framework fully embedded within the QAOM paradigm. The proposed solution introduces a mathematically consistent metric for comparing linguistic assessments, an entropy-based attribute weighting approach rooted in qualitative information, and an aggregation process that reflects expert diversity. Furthermore, a specialized normalization protocol is developed to handle negative attributes across heterogeneous scales. The feasibility and advantages of the method are validated through comprehensive examples and comparative analyses, highlighting improvements over traditional techniques in terms of objectivity, flexibility, and analytical depth. Overall, these developments markedly enhance the capabilities of QAOM-based MAGDM, equipping decision-makers with more nuanced and reliable tools for tackling complex problems characterized by imprecision and divergent expert opinions.
Applied Article
Control Theory & Systems
Maha Mohsin Mohammed Ali; Mahmoud Mahmoudi; Majid Darehmiraki
Abstract
This study addresses the numerical solution of an optimal control problem governed by a fractional convection–reaction–diffusion partial differential equation. The approach utilizes Radial Basis Function–Partition of Unity (RBF-PU) methods combined with the Grünwald-Letnikov approximation ...
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This study addresses the numerical solution of an optimal control problem governed by a fractional convection–reaction–diffusion partial differential equation. The approach utilizes Radial Basis Function–Partition of Unity (RBF-PU) methods combined with the Grünwald-Letnikov approximation for fractional derivatives, which provides a fundamental extension of classical derivatives in fractional calculus. To enhance sparsity in the control strategy, an $L_2$ norm is integrated into the objective function, along with quadratic penalties to reduce deviations from the desired state. This hybrid formulation facilitates the effective management of spatially sparse controllers, relevant in many practical applications. The RBF-PU technique offers a flexible and efficient framework by partitioning the domain into overlapping subregions, applying local RBF approximations, and synthesizing the global solution with compactly supported weight functions. Numerical experiments demonstrate the accuracy and effectiveness of this method.
Research Article
Optimization & Operations Research
Michael Oluwaseun Ayansiji; Friday Zinzendoff Okwonu
Abstract
Hyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive. We present Interpolation-Based Optimization (IBO), a ...
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Hyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive. We present Interpolation-Based Optimization (IBO), a novel framework that employs piecewise polynomial interpolation to estimate optimal hyperparameters from sparse evaluations efficiently. IBO achieves substantial computational savings by constructing deterministic interpolants with linear per-iteration complexity of O(n.d^3), in contrast to the cubic O(n^3) cost associated with BO. Empirical studies on the MNIST dataset show that IBO attains 98.0% accuracy with a 39% reduction in runtime (12 iterations vs. 18) and no statistically significant difference from BO, p = 0.12. In higher-dimensional, lower-cost settings, such as ResNet-18 on CIFAR-10, performance degrades, highlighting a trade-off between dimensionality and efficiency. More generally, IBO is well-suited for resource-constrained settings due to its simplicity, determinism, and computational efficiency. Future work will explore hybrid methods to address scalability problems and extend IBO to more complex modeling architectures, such as transformers.
Research Article
Control Theory & Systems
Maria Afsharirad
Abstract
This paper presents a hybrid scheme for solving optimal control problems. Discretizing the time interval and assuming a constant control value on each sub-interval transforms the optimal control problem into an assignment problem. To cluster feasible solutions, a novel method is proposed ...
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This paper presents a hybrid scheme for solving optimal control problems. Discretizing the time interval and assuming a constant control value on each sub-interval transforms the optimal control problem into an assignment problem. To cluster feasible solutions, a novel method is proposed in this paper, which applies metaheuristic algorithms—specifically, genetic algorithms and particle swarm optimization—to generate a large number of solutions. Subsequently, the K-means clustering method is employed to classify these solutions into clusters. Enhancing the median of each cluster, using metaheuristic techniques, ultimately results in improved medians. The best median from the final iteration of the algorithm serves as an acceptable solution for the optimal control problem. In some cases, it even succeeds in discovering a new best solution.
Research Article
Control Theory & Systems
Mohammad Alsaeedi; Mostafa Tavakolli; Ahmad Abouyee; Khatere Ghorbani Moghadam; Reza Ghanbari
Abstract
In this study, we proposed a novel graph partitioning problem where the edges are characterized by trapezoidal fuzzy numbers. A linear ranking function is employed to establish an order among these fuzzy numbers. We derive the necessary conditions for the existence of an ...
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In this study, we proposed a novel graph partitioning problem where the edges are characterized by trapezoidal fuzzy numbers. A linear ranking function is employed to establish an order among these fuzzy numbers. We derive the necessary conditions for the existence of an optimal solution to this problem. To address the fuzzy graph partitioning problem, we implement and compare the performance of three algorithms: Genetic Algorithm, Tabu Search, and Sequential Least Squares Programming. The algorithms are evaluated based on objective values, computational time, and the number of iterations across multiple numerical examples. Utilizing Dolan-Moré performance profiles, we demonstrate the superiority of our proposed approach relative to existing methods. The findings highlight the robustness and computational efficiency of our methodology, making a meaningful contribution to the advancement of fuzzy graph algorithms and their practical applications.
Research Article
Control Theory & Systems
Reza Dehghan
Abstract
The orthogonal polynomials approximation method is widely regarded as a highly effective and versatile technique for solving optimal control problems in nonlinear systems. This powerful approach has found extensive applications in both theoretical research and practical engineering, demonstrating ...
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The orthogonal polynomials approximation method is widely regarded as a highly effective and versatile technique for solving optimal control problems in nonlinear systems. This powerful approach has found extensive applications in both theoretical research and practical engineering, demonstrating its capability to address complex dynamical behaviors. In this paper, we thoroughly investigate the optimal control problem of the Van der Pol oscillator, a classic nonlinear system with broad scientific and engineering relevance. The proposed solution follows two distinct and systematic steps. First, the state and control functions are approximated by linear combinations of shifted Chelyshkov polynomials, whose coefficients are treated as unknown parameters to be determined. Second, the resulting transformed problem is formulated as a nonlinear optimization problem and efficiently solved using advanced numerical optimization tools implemented in \textsc{Matlab}. To demonstrate the accuracy and robustness of the proposed approach, we present and analyze numerical results across several representative scenarios.
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