Search Results - feed-forward ((optimization algorithm) OR (((prediction algorithm) OR (selection algorithm))))
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A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction
Published 2025Subjects:Article -
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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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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Early detection of dengue disease using extreme learning machine
Published 2018“…Therefore, this research proposed an improved algorithm known as ELM which is an extension of Feed Forward Neural Network that utilize the Moore Penrose Pseudoinver matrix that gain the optimal weights of neural network architecture. …”
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Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices
Published 2014“…The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities.…”
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Proceeding Paper -
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Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm
Published 2013“…The potential of genetic algorithm in optimization was utilized in the proposed hybrid model to minimize the error prediction for regions of cutting conditions away from the Taguchi based factor level points. …”
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A novel scheme for spectrum prediction in cognitive radio networks / Mehdi Askari and Rezvan Dastanian
Published 2021“…In this model, a novel improved version of Teaching-Learning-Based-Optimization algorithm, also referred to iTLBO algorithm, is proposed to train a feed forward artificial neural network (ANN). …”
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Development of generalized feed forward network for predicting annual flood (depth) of a tropical river
Published 2014“…This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. …”
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Optimization of robot plasma coating efficiency using genetic algorithm and neural networks / S.Prabhu and B.K.Vinayagam
Published 2017“…This work describes the Taguchi analysis coupled with Artificial Neural network and Genetic algorithm to optimize the robot deposition parameters used for plasma coating on titanium aluminum alloy material. …”
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Forecasting solar power generation using evolutionary mating algorithm-deep neural networks
Published 2024“…This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. …”
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Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
Published 2016“…Precise water level prediction assists in practical and optimal usage of water resources. …”
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Designing of prediction model for parameter optimization in cnc machining based on artificial neural network / Armansyah ... [et al.]
Published 2025“…These outputs were then utilized to train an ANN prediction model based on a feed-forward backpropagation (FFBP) algorithm. …”
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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review
Published 2023“…Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review…”
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Skin Hydration And Transepidermal Water Loss Measurement Using Vis/nir Spectroscopy And Feed-forward Backpropagation Neural Network
Published 2022“…Thus, this study proposed the use of visible/near-infrared (VIS/NIR) spectroscopy, specifically at wavelengths 698 nm, 970 nm, 1200 nm, 1450 nm, and 1950 nm to predict the skin hydration and TEWL. In this study, the prediction models were built using simple linear regression, multiple regression analysis, and feed-forward backpropagation neural network (FFBPNN) with gradient descent algorithm. …”
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An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
Published 2016“…Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. …”
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Wind power prediction using Artificial Neural Network: article
Published 2010“…In order to get an accurate wind power prediction, several network structures, training algorithms and transfer functions have been developed and tested with different sets of data. …”
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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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Optimisation of laser cutting parameters of oil palm wood / Harizam Mohd Zin
Published 2013“…The potential of genetic algorithm in optimization was utilized in the proposed hybrid model to minimize the error prediction for regions of cutting conditions away from the Taguchi based factor level points. …”
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