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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN2SAT). …”
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Heat exchanger network optimization using differential evolution with stream splitting
Published 2014“…This article introduces a new strategy for HEN optimization using differential evolution algorithm. The proposed method considers splitting stream at the pinch point, to minimize the total cost of the network. …”
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Dynamic smart grid communication parameters based cognitive radio network
Published 2023“…The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. …”
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A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles
Published 2017“…Particle Swarm Optimization has been used in the proposed algorithm to minimize the fitness function. …”
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Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim
Published 2007“…Roadways and telephone systems are the examples of them. Genetic Algorithms (GA), pioneered by John Holland, applies the principle of evolution found in nature to the problem of finding an optimal solution. …”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
Published 2020“…Hence, in this work, we design an improved Grasshopper Optimization Algorithm (GOA) based ESN. The proposed technique uses a new solution representation with a simplified attraction and repulsion mechanisms to enhance performance. …”
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An improved firefly algorithm for optimal microgrid operation with renewable energy
Published 2017“…This project proposes an Improved Firefly Algorithm (IFA), which is a improvement of classical Firefly Algorithm (FA) technique using characteristic approach of Lévy flights to solve the optimal microgrid operation. …”
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Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…After determine the rates another single objectives algorithm is tested. Hence, the second sub-objective is 2) to evolve RTS controllers using DE and FFNN. …”
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Optimal selection of location, sizing and power factor for solar PV plants using differential evolution
Published 2023Conference Paper -
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OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM
Published 2011“…Typically, the PID controller is used and has evolved into more recent approaches like the hybrid with fuzzy logic controller (FLC) or neural network (NN). …”
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Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons
Published 2018“…In this study, the uncertainty and nonstationary characteristics of streamflow data has been treated using a set of coupled data pre-processing methods before being considered as input for an artificial neural network algorithm namely; rolling mechanism (RM) and grey models (GM). …”
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A hybrid optimization technique for solving economic emission load dispatch problems
Published 2023text::Final Year Project
