Search Results - (( using simulation learning algorithm ) OR ( using factorization learning algorithm ))

Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition by Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.

“…These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). …”

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency by Ramli, Muhammad Shahrul Azwan, Zainal Abidin, Mohamad Shukri, Hasan, Nor Shahida, Md Reba, Mohd Nadzri, Kolawole, Keshinro Kazeem, Ardiansyah, Rizqi Andry, Mpuhus, Sikudhan Lucas

“…The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. …”
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Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

“…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”

The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

“…The results show that the proposed algorithm extensively improves the learning process of conventional Back Propagation algorithm.…”
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Combination of perturb and observe with online sequential extreme learning machine for photovoltaic system maximum power point tracking by Dira, Yasir Sabah

“…From different MPPT techniques previously proposed, the online sequential extreme learning machine algorithm and conventional perturb and observe are combined together as a proposed MPPT algorithm. …”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

“…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

“…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Soomro, Zubair Adil, Adrianshah, Andi

“…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi

“…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

“…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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Random forest algorithm for co2 water alternating gas incremental recovery factor prediction by Belazreg, L., Mahmood, S.M., Aulia, A.

“…The aim of this paper is using an ensemble machine learning algorithm to develop a WAG incremental recovery factor predictive model that can be used by reservoir engineers to estimate WAG incremental recovery factor prior kick-off of laboratory experiments and comprehensive technical studies. …”
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Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning by Mohamad Hafiz Abu Bakar, Mohamad Hafiz Abu Bakar, Abu Ubaidah Shamsudin, Abu Ubaidah Shamsudin, Ruzairi Abdul Rahim, Ruzairi Abdul Rahim, Zubair Adil Soomro, Zubair Adil Soomro, Andi Adrianshah, Andi Adrianshah

“…According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.…”
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

“…The proposed algorithm employs a combined model that uses two different measures (nonconformity metric measures and Local Distance-based Outlier Factor (LDOF)) to improve its detection ability. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…The second test evaluates IFS in a controlled study using simulated datasets. Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
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Acquisition of context-based word recognition by reinforcement learning using a recurrent neural network by Ahmad Afif, Mohd Faudzi

“…The learning system was trained on several tasks using simulations or real time learning in order to verify whether flexible recognition could emerge through this context-based word recognition learning. …”
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Acquisition of context-based word recognition by reinforcement learning using a recurrent neural network by Ahmad Afif, Mohd Faudzi

“…The first type using ideal- images as learning data and the second one using real camera captured images. …”
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Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm by Mohd Kasihmuddin, Mohd Shareduwan, Abdul Halim, Nur Shahira, Mohd Jamaludin, Siti Zulaikha, Mansor, Mohd. Asyraf, Alway, Alyaa, Zamri, Nur Ezlin, Azhar, Siti Aishah, Marsani, Muhammad Fadhil

“…In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. …”
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A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Ali Abbas, Kassim

“…Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. …”
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Box-jenkins and genetic algorithm hybrid model for electricity forecasting system by Mahpol, Khairil Asmani

“…By adopting the GA blind search, the algorithm combines searching techniques and their capabilities to learn about the relationship of the pattern-recognition of the past data. …”
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Review of Wheat Disease Classification and Severity Detection Models by Hongyan, Zang, Annie, Joseph, Shourong, Zhang, Rong, Liu, Wanzhen, Wang

“…To combat these diseases, researchers have been studying the use of advanced techniques such as deep plant disease learning and image processing methods for identification. …”
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