Search Results - (( data application during algorithm ) OR ( parameter optimization model algorithm ))
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025Subjects:Article -
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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)
Published 2019“…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…The ADAM optimizer effectively tackles challenges in continuous parameter optimization by dynamically updating the model's weights and biases, adapting the learning rate for each parameter based on accumulated historical gradient information to achieve more efficient minimization of the loss function during training. …”
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…DM is optimized using genetic algorithm (GA). GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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6
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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7
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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8
Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Although various optimization algorithms have been widely employed in multiple applications, the traditional Archimedes optimization algorithm (AOA) has presented imbalanced exploration with exploitation phases and the propensity for local optima entrapment. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab
Published 2025“…The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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10
Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat
Published 2024“…In addition, Coati Optimization algorithm, Particle Swarm Opimisation (PSO) and Bayesian Optimsiation (BO) are integrated to identify optimal parameters and minimize settlement during twin tunnel excavation and GBT with the optimisation algorithm has shown consistent capability identifying the least SS induced by twin tunnels Keyword: …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Optimizers play an essential role in adjusting the model’s parameters to minimize errors, assisting the learning process during the model development. …”
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Proceeding Paper -
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Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data
Published 2009“…The fault identification prior to reclosing is based on optimized artificial neural network associated with three different training algorithms. …”
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Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
Published 2021“…The proposed data modelling applies an additional class-specific fuzzy weight information to new data vectors during the classification process. …”
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VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
Published 2012“…It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. …”
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Early detection of dengue disease using extreme learning machine
Published 2018“…The proposed ELM prevents several backpropagation issues by reducing the used of many parameters that solves the main drawbacks of Backpropagation algorithm that uses during the training phase of Neural Network. …”
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Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw
Published 2015“…In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). …”
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Multi agent quality of service routing based on scheme ant colony optimization algorithm
Published 2014“…It can also improve the throughput in link failure state by about 25% and decrease the packet loss rate by 27% and 39% before and during link failure respectively. Moreover, the proposed approach outperforms the WSP and SWP algorithms, where network efficiency in saturated load gains an 8.39% improvement while the satisfaction of delay and jitter constraints for time critical applications are also achieved. …”
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Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
Published 2006“…The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. …”
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Final Year Project Report / IMRAD -
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…T, the algorithm has the capability to statistically validate the algorithm to obtain the optimal parametric model to represent the failure of the component against the actual time to failure data from the local automobile industry. …”
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