Search Results - (( discrete simulation method algorithm ) OR ( based optimization method algorithm ))
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Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
Published 2023“…Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. The Binary Artificial Bee Colony will be utilized to optimize the logical structure according to the ratio of negative literals by capitalizing the features of the exploration mechanism of the algorithm. …”
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…Evolutionary computation (EC) is known to be an effective search and optimization method and in this paper EC is proposed as a model structure selection algorithm. …”
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Optimization of Digital Electronic Circuit Structure Design Using Genetic Algorithm
Published 2008“…The results show that the proposed method is able to produce the optimized circuit with lesser number of gates compared to the conventional methods. …”
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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5
A new ant based rule extraction algorithm for web classification
Published 2011“…Web documents contain enormous number of attributes as compared to other type of data. Ant-Miner algorithm is also still lacking in efficiency, accuracy and rule simplicity because of the local minima problem.Therefore, the Ant-Miner algorithm needs to be improved by taking into consideration of the accuracy and rule simplicity criteria so that it could be used to classify Web documents data sets or any large data sets.The best attribute selection method for Web texts categorization is the combination of correlation-based evaluation with random search as the search method.However, this attribute selection method will not give the best performance in attributes reduction. …”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…he genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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A novel single parent mating technique in genetic algorithm for discrete - time system identification
Published 2024“…A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. …”
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Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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Voting algorithms for large scale fault-tolerant systems
Published 2011“…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…Performance indicators included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Parameter Magnitude-based Information Criterion 2 (PMIC2). The findings showed that incorporating SPM with single-point crossover resulted in lower objective function (OF) values and improved error indices (EI) compared to traditional GA methods. …”
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13
Optimal resource allocation for NOMA wireless networks
Published 2022“…When compared to existing methods, simulation results reveal that the suggested technique has a better power allocation capability.…”
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Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications
Published 2020“…In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner. …”
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Deep Reinforcement Learning For Control
Published 2021“…The complete project is carried out in the CARLA simulator to determine how to operate in discrete action space using Deep Reinforcement Learning (DRL) algorithms. …”
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Monograph -
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Restoration planning strategy of transmission system based on optimal energizing time of sectionalizing islands / Dian Najihah Abu Talib
Published 2019“…There are two discrete optimization techniques used in this work, which are the Artificial Bee Colony algorithm and Evolutionary Programming. …”
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Integration Of Travel Time Zone For Optimal Siting Of Emergency Facilities
Published 2008“…Fire stations in Jakarta Selatan were chosen for simulation. Two algorithms, Greedy Adding (Add) and Greedy Adding with Travel Time Evaluation (GAT), were applied to solve the optimization problem of the MSAP. …”
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Resource management algorithms for wavelength division multiplexing optical metro slotted ring networks
Published 2012“…The performance evaluation of the proposed algorithms has been done through a detailed and extensive discrete-event-simulation analysis. …”
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Improvement of LMS adaptive noise canceller using uniform poly-phase digital filter bank
Published 2020“…Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. …”
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A discrete simulated kalman filter optimizer for combinatorial optimization problems
Published 2022“…An example of a numerical algorithm is the simulated Kalman filter (SKF). Various method has been introduced as an extension of a numerical algorithm to adapt it to a discrete search space. …”
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