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.
Control Theory & Systems
Fatemeh Babakordi; Nemat Allah Taghi-Nezhad
Abstract
This paper presents the introduction of two novel equation types: the partial hesitant fuzzy equation and the half hesitant fuzzy equation. Additionally, an efficient method is proposed to solve these equations by defining four solution categories: Controllable, Tolerable Solution ...
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This paper presents the introduction of two novel equation types: the partial hesitant fuzzy equation and the half hesitant fuzzy equation. Additionally, an efficient method is proposed to solve these equations by defining four solution categories: Controllable, Tolerable Solution Set (TSS), Controllable Solution Set (CSS), and Algebraic Solution Set (ASS). Furthermore, the paper establishes eight theorems that explore different types of solutions and lay out the conditions for the existence and non-existence of hesitant fuzzy solutions. The practicality of the proposed method is demonstrated through numerical examples.
Fatemeh Babakordi; Tofigh Allahviranloo
Abstract
Solving fuzzy linear systems has been widely studied during the last decades. However, there are still many challenges to solving fuzzy linear equations, as most of the studies have used the principle of extension, which suffers from shortcomings such as the lack ...
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Solving fuzzy linear systems has been widely studied during the last decades. However, there are still many challenges to solving fuzzy linear equations, as most of the studies have used the principle of extension, which suffers from shortcomings such as the lack of solution, achieving solutions under very strong conditions, large support of the obtained solutions, inaccurate or even incorrect solutions due to not utilizing all the available information, complicated process and high computational load. These problems motivated us to present a fuzzy Cramer method for solving fuzzy linear equations, which uses arithmetic operations based on the Transmission Average (TA). In this study, fully fuzzy linear systems in the form of $ \tilde{A}\tilde{X}=\tilde{B} $, and dual fuzzy linear systems in the form of $ \tilde{A}\tilde{X}+\tilde{B}=\tilde{C}\tilde{X}+\tilde{D} $ are solved using the proposed fuzzy Cramer method, and numerical examples are provided to confirm the effectiveness and applicability of the proposed method.
