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1
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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2
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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3
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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4
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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5
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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6
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction
Published 2023“…The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. …”
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7
Gradient method with multiple damping for large-scale unconstrained optimization
Published 2019“…That is, the proposed method is constructed by combining damping with line search strategies, in which an individual adaptive parameter is proposed to damp the gradient vector while line searches are used to reduce the function value. …”
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Minimization of torque ripple and flux droop using optimal DTC switching and sector rotation strategy
Published 2022“…A five-level cascaded H-bridge (CHB) inverter was used in the optimal DTC switching strategy because it had many voltage vectors and could be used for a variety of speed operations. …”
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Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction
Published 2019“…The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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11
Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…The rationale behind developing algorithms using real encoding of chromosome representations is the limitations of binary encoding. …”
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12
Classification of heart disease with machine learning: a comparison of grid search, random search, and Bayesian Optimization
Published 2026“…The results of the study show that hyperparameter optimization significantly improves prediction accuracy compared to baseline models, with the optimal method varying across algorithms. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…Five state-of-the-art FS methods are used to evaluate the effectiveness of proposed methods in this work. …”
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Towards large scale unconstrained optimization
Published 2007“…The matrix-storage free BFGS (MF-BFGS) method is a method that combines with a restarting strategy to the BFGS method. …”
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15
Improved Direct Torque Control (DTC) Performances Of Induction Machine Using Cascaded H-Bridge Multilevel Inverter
Published 2017“…The proposed DTC control algorithm can be optimally executed at high computation rate by totally using C-coding with DS1104 controller board. …”
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16
Quasi-Newton type method via weak secant equations for unconstrained optimization
Published 2021“…The possible variants of matrix free quasi-Newton methods are further explored, using the weak secant equation. …”
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17
Modeling and implementation of space vector modulation for three-phase direct torque control matrix converter
Published 2013“…Widespread, systematic, and in-depth studies have been focused on the modulation algorithm and the commutation strategy of the MC, and the key technologies for its application in induction motor drive system. …”
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18
Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…This involves sensor based closed loop vector control for a healthy IM, Variable frequency with constant flux(V/F) closed loop for both stator open winding and stator short winding faults , V/F open loop to control the drive in case of minimum voltage fault and using the sensorless vector control in case of encoder faults. …”
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Short term electricity price forecasting with multistage optimization technique of LSSVM-GA
Published 2023“…Therefore, a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features. …”
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Short Term Electricity Price Forecasting With Multistage Optimization Technique Of LSSVM-GA
Published 2017“…Price prediction has now become an important task in the operation of electrical power system.In short term forecast,electricity price can be predicted for an hour-ahead or day-ahead.An hour-ahead prediction offers the market members with the pre-dispatch prices for the next hour.It is useful for an effective bidding strategy where the quantity of bids can be revised or changed prior to the dispatch hour.However,only a few studies have been conducted in the field of hour-ahead forecasting.This is due to most of the power markets apply two-settlement market structure (day-ahead and real time) or standard market design rather than singlesettlement system (real time).Therefore,a multistage optimization for hybrid Least Square Support Vector Machine (LSSVM) and Genetic Algorithm (GA) model is developed in this study to provide an accurate price forecast with optimized parameters and input features.So far,no literature has been found on multistage feature and parameter selections using the methods of LSSVM-GA for hour-ahead price prediction.All the models are examined on the Ontario power market;which is reported as among the most volatile market worldwide.A huge number of features are selected by three stages of optimization to avoid from missing any important features.The developed LSSVM-GA shows higher forecast accuracy with lower complexity than the existing models.…”
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