Search Results - (( parameter optimization method algorithm ) OR ( learning applications using algorithm ))
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1
Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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A review of training methods of ANFIS for applications in business and economic
Published 2016“…Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. …”
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A review of training methods of ANFIS for applications in business and economics
Published 2016“…Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. …”
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4
Advances of metaheuristic algorithms in training neural networks for industrial applications
Published 2023Article -
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. …”
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6
Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…PSO-EBGWO is used to optimize the parameters of the CatBoost model. …”
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Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. …”
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8
A hybridization of butterfly optimization algorithm and harmony search for fuzzy modelling in phishing attack detection
Published 2023“…Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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10
Accelerated mine blast algorithm for ANFIS training for solving classification problems
Published 2016“…ANFIS accuracy depends on the parameters it is trained with. Keeping in view the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm, many have trained ANFIS using metaheuristic algorithms. …”
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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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12
Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network
Published 2020“…Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
Published 2019“…ReliefF can solve the problem of large feature dimension in the existing RKELM. By using clustering method K-Means, we have found the best center point position to calculate Kernel matrix. at last, we have employed Quantum-behaved Particle Swarm Optimization (QPSO) to get the optimal kernel parameter in the proposed model. …”
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16
Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing.…”
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17
An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system
Published 2011“…The idea is to use GA as an optimizer to find the optimal values of hyper-parameters of LS-SVM and adopt a supervised learning approach to train the LS-SVM model. …”
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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer
Published 2024“…In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…To summarise, metaheuristic algorithms can give a superior optimization approach than the traditional artificial neural network method, providing the computing time is within an acceptable range. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
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