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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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Improving neural networks training using experiment design approach
Published 2005“…There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. …”
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The effect of pre-processing techniques and optimal parameters on BPNN for data classification
Published 2015“…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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4
Maximum 2-satisfiability in radial basis function neural network
Published 2020“…This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). …”
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Heterogenous adaptive ant colony optimization with 3-opt local search for the travelling salesman problem
Published 2020“…The majority of optimization algorithms require proper parameter tuning to achieve the best performance. …”
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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“…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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7
Application of conjugate gradient approach for nonlinear optimal control problem with model-reality differences
Published 2018“…In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. …”
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Application of conjugate gradient approach for nonlinear optimal control problem with model-reality difference
Published 2018“…In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. …”
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Machining optimization using Firefly Algorithm / Farhan Md Jasni
Published 2020“…Based on the previous research on the success of Firefly Algorithm, this approach will be able to optimize the machining parameter of milling operation. …”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. …”
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11
Characterization of PV panel and global optimization of its model parameters using genetic algorithm
Published 2023“…This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. …”
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Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin
Published 2014“…In this paper, an efficient bandwidth allocation algorithm for the uplink traffic in mobile WiMAX is proposed. …”
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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
Published 2017“…In this paper, heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems. …”
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Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
Published 2021“…However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. …”
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems
Published 2011“…The values of these adjustable parameters are updated repeatedly. In this way, the optimal solution of the model will approach to the true optimum of the original optimal control problem. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. This is to remedy the problem of using the existing Min-Max (MM) and Decimal Scaling (DS) techniques, which have overflow weakness. …”
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19
Hyper-Heuristic Evolutionary Approach for Constructing Decision Tree Classifiers
Published 2021“…Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. …”
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