Search Results - (( a simulation optimization algorithm ) OR ( parameter activation function algorithm ))
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. …”
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A harmony search-based learning algorithm for epileptic seizure prediction
Published 2016“…The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. …”
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). …”
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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Optimal power flow using hybrid firefly and particle swarm optimization algorithm
Published 2020“…The particular fitness function is chosen as a single objective based on control parameters. …”
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The algorithm is effectively applied in activated sludge system with improved dynamic performances in effluent quality index and energy consumed of Benchmark Simulation Model No.1.…”
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Optimization of supply chain management by simulation based RFID with XBEE Network
Published 2015“…This algorithm computes the optimal results of objective functions in a scientific manner. …”
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Dynamic smart grid communication parameters based cognitive radio network
Published 2023“…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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Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
Published 2016“…A genetic algorithm (GA) was combined with artificial neural networks (ANN), designated as neuro-genetic algorithm (NGA) in this study, to determine the effective number of nodes and optimal activated functions (FAs) in an ANN structure. …”
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Neural Network – A Black Box Model
Published 2024“…Three learning paradigms of ANN include supervised, unsupervised, and reinforcement learning. A variety of metaheuristic algorithms have been used to train ANN, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Tabu Search (TS), and Harmony Search (HS). …”
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Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system
Published 2024“…Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. With the GWO technique and the integral of time absolute error (IAE) as a fitness function, the three gain parameters of the Fuzzy PID controller – Kp, Ki, and Kd– have been optimally set. …”
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Design of self-tuning minimum effort active noise control with feedback inclusion architecture
Published 2009“…It was found that the optimal controller designed using this technique which is constrained only by the accuracy of the two models identified using recursive least squares algorithm, yields good cancellation level. …”
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Time-variant online auto-tuned pi controller using pso algorithm for high accuracy dual active bridge dc-dc converter
Published 2022“…Moreover, the fixed gains in offline tuning cannot fully control the system output as needed and do not guarantee the robustness of the system. This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. …”
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Performance Analysis of Active Power Filter for Harmonic Compensation using PI-PSO
Published 2015“…The simulated system is a three phase balanced voltage system with nonlinear load .A particle swarm optimization (PSO) is implemented to optimize the gains of a proportional-integral (PI) algorithm to control the SAPF. …”
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Time-Variant online Auto-Tuned PI controller using PSO algorithm for high accuracy dual active bridge DC-DC converter
Published 2022“…Moreover, the fixed gains in offline tuning cannot fully control the system output as needed and do not guarantee the robustness of the system. This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. …”
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Single fitness function analysis of energy-consumption and radio bandwidth management in coverage area problems
Published 2024thesis::master thesis -
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Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios
Published 2015“…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. To determine the optimum solution in each policy, real coded genetic algorithm is used as an optimization technique. …”
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Development of optimized maintenance scheduling model for coal-fired power plant boiler
Published 2023“…Literature revealed that mathematical methods and metaheuristic algorithms are common approaches in solving combinatorial optimization problems with a large search space in a reasonable computational run time. …”
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