Search Results - (( _ distribution new algorithm ) OR ( parameter estimation ((acs algorithm) OR (ant algorithm)) ))

Modeling the impacts of constant price GDP and population on CO2 emissions using Cobb-Douglas model and ant colony optimization algorithm by Hafizan, Juahir, Sukono, ., Subartini, B, Thalia, P, Supian, S., Lesmana, E, Budiono, R

“…Modeling is done by using Cobb-Douglas model production function, where parameter estimation is done by using ant colony optimization algorithm. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA) by Ponnalagu, Dharswini, Mohd Ashraf, Ahmad, Jui, Julakha Jahan

“…Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. …”
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Ant colony optimization (ACO) technique for reactive power planning in power system stability assessment / Mohd Rozely Kalil by Kalil, Mohd Rozely

“…ACO is a new cooperative agent’s approach, which is inspired by the observation of the behaviours of real ant colonies on the topics of ant trial formation and foraging method. …”
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Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm by Banadkooki F.B., Ehteram M., Ahmed A.N., Teo F.Y., Ebrahimi M., Fai C.M., Huang Y.F., El-Shafie A.

Ant colony optimization and genetic algorithm models for suspended sediment discharge estimation for gorgan-river, Iran by Mohammad Reza Pour, Omolbani

“…New models based on artificial intelligence models, namely; Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are now being used more frequently to solve optimization problems. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Indirect Rotor Field Oriented Control of Induction Motor With Rotor Time Constant Estimation by Moh'd Radwan, Eyad Moh'd

“…Since the position of the rotor flux vector is estimated in an IRFOC scheme, and is dependent on the motor model (more specifically the rotor parameters), these parameters must be obtained accurately and match the motor parameters at all times. …”
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Sensor-less vector control using adaptive observer scheme for controlling the performance of the induction motor / Mazhar Hussain Abbasi by Mazhar, Hussain Abbasi

“…Internal parameters are used, for example, feed-forward compensator of current controller and parameters of observer model in sensor less position. …”
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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

The largest studentized residual test for bad data identification in state estimation of a power system by Khan, Z., Razali, R.B., Daud, H., Nor, N.M., Firuzabad, M.F.

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Optimized scheduling for an airconditioning system based on indoor thermal comfort using the multiobjective improved global particle swarm optimization by Haniff, Mohamad Fadzli, Selamat, Hazlina, Khamis, Nuraqilla, Alimin, Ahmad Jais

“…The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. …”
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Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data by Jayapal V., Zain A.F.M.

Ancestral dynamic voting algorithm for mutual exclusion in partitioned distributed systems by Zarafshan, Faraneh, Karimi, Abbas, Al-Haddad, Syed Abdul Rahman, Saripan, M. Iqbal, Subramaniam, Shamala

“…In this study, a new dynamic algorithm is presented as a solution for mutual exclusion in partitioned distributed systems. …”
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A new technique for the reconfiguration of radial distribution network for loss minimization by Shamsudin, Nur Hazahsha, abidullah, Noor Athira, Abdullah, Abdul Rahim, Sulaima, Mohamad Fani, Jaafar, Hazriq Izzuan

“…In this paper, a new technique called as Improved Genetic Algorithm (IGA) for reconfiguring distribution network simultaneously implemented with the placement of small scale power generation or Distributed Generation (DG) is presented. …”
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Evaluation of reference signal estimation techniques for the control of shunt active power filter by Atan N.B., Yunus B.B.

The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization by Abbaszadeh M., Ehteram M., Ahmed A.N., Singh V.P., Elshafie A.

A new efficient checkpointing algorithm for distributed mobile computing by Mansouri, Houssem, Badache, Nadjib, Aliouat, Makhlouf, Pathan, Al-Sakib Khan

“…Considering this issue, the contribution in this paper is a proposal of a new checkpointing algorithm suitable for mobile computing systems. …”
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Loss reduction in distribution networks using new network reconfiguration algorithm by Kashem, M.A., Moghavvemi, M., Mohamed, A., Jasmon, G.B.

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Optimal location and size estimation of distributed generators by employing grouping particle swarm optimization and grouping genetic algorithm by Mohammed, Zahraa Abdulkareem

“…This work is aimed to decrease the total real and reactive power losses while enhancing the voltage profile of the distribution network with less computation time by proposing two new artificial intelligence algorithms, i.e. grouping particle swarm optimization algorithm and grouping genetic algorithm. …”
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Fault-current predetermination using time- limited CT secondary side measurements by the covariance-Prony method by Kamel , Nidal

“…Samples of the CT secondary current are collected until the onset of saturation, and are operated upon using a covariance-Prony algorithm to estimate the aforementioned critical parameters. …”
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