Search Results - (( layer perceptron based algorithm ) OR ( java application using algorithm ))

Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks by Sadiq, A., Yahya, N.

“…The proposed fractional back-propagation multi-layer perceptron (FBP-MLP) method is based on fractional calculus and it utilizes the concept of fractional power gradient which provides complementary information about the cost function that helps in rapid convergence. …”
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Comparison between Levenberg-Marquardt and scaled conjugate gradient training algorithms for ionosphere condition using Multilayer Perceptrons: article / Mohd Sharif Ibrahim by Ibrahim, Mohd Sharif

“…Based on the result, we conclude that both algorithms were comparable in terms of accuracy and speed. …”
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Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

“…In this research, a vision system algorithm has been developed to identify and locate base of young corn trees based upon robot vision technology, pattern recognition techniques, and knowledge-based decision theory. …”
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Comparison between Levenberg-Marquardt and scaled conjugate gradient training algorithms for ionosphere condition using Multilayer Perceptrons / Mohd Sharif Ibrahim by Ibrahim, Mohd Sharif

“…Based on the result, there are concluded that both algorithms were comparable in terms of accuracy and speed. …”
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Classification of multichannel EEG signal by single layer perceptron learning algorithm by Hasan, Mohammad Rubaiyat, Ibrahimy, Muhammad Ibn, Motakabber, S. M. A., Shahid, Shahjahan

“…Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Single Layer Perceptron Learning (SLPL) algorithm has a very low computational requirement which makes it suitable for online BCI system. …”
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Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm by Pham Q.B., Afan H.A., Mohammadi B., Ahmed A.N., Linh N.T.T., Vo N.D., Moazenzadeh R., Yu P.-S., El-Shafie A.

Genetic algorithms-based quality of service service selection in cloud computing using multilayer perceptron by Mosallanejad, Ahmad, Behjat, Amir Rajabi, Mustapha, Aida, Atan, Rodziah

“…To address this issue, this paper proposes an effective services classification in cloud environment, which will classify the equivalent services based on their quality of service (QoS). The attribute selection method is based genetic algorithms (GA) and is designed to rank the cloud services before the attributes are being fed into a multi-layer perceptron (MLP) classification system. …”
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System identification of hammerstein model a quarter car passive suspension systems using Multilayer Perceptron Neural Networks (MPNN) by Hanafi, Dirman, Rahmat, Mohd. Fua'ad

“…The Networks Structure Is Based On System Model. The Network Learning Algorithm Is Based On Fisher’s Scoring Method. …”
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Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid by Ab Hamid, Salbiah

“…It learns the types of input based on their weights and properties. MLP consist of interconnected input layer, hidden layer and output layer. …”
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Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms by Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.

Predicting longitudinal dispersion coefficient using ensemble models and optimized multi-layer perceptron models by Gholami M., Ghanbari-Adivi E., Ehteram M., Singh V.P., Najah Ahmed A., Mosavi A., El-Shafie A.

Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm by Ehteram M., Ahmed A.N., Ling L., Fai C.M., Latif S.D., Afan H.A., Banadkooki F.B., El-Shafie A.

Interactive learning package for artificial neural network (Demonstration Module) / Camellia Mohd Kamal by Camellia , Mohd Kamal

“…For Perception there will be the Description Neuron Model, Perceptron Basic Architecture and perceptron Algorithm with one example of solved problem. …”
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Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining by Sivarao, Subramonian

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Rule extraction from multi-layer perceptron neural network using decision tree for currency exchange rates forecasting by Soleh, Ardiansyah

“…Thus, the aim of this study was to extract valuable information (rule) from trained multi-layer perceptron (MLP) neural networks using decision tree. …”
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Model reference adaptive control based on MLP network for dynamic system by Norhayati, Mohd Noor, Mohd Yusoff, Mashor, Siti Maryam, Sharun, Wan Nur Hadani, Soh, Waren

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A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem by Ahmed A.N., Van Lam T., Hung N.D., Van Thieu N., Kisi O., El-Shafie A.

Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

“…The RBFNN model with softmax as the hidden layer activation function and identity as the outer layer activation function has the least predictive performance, as indicated by an R2 of 0.403 and a RMSE of 301.55. …”
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KM-NEU: an efficient hybrid approach for intrusion detection system by Lisehroodi, Mazyar Mohammadi, Muda, Zaiton, Yassin, Warusia, Udzir, Nur Izura

“…To overwhelm this challenge a new hybrid learning approach, KM-NEU is proposed by combination of K-means clustering and Neural Network Multi-Layer Perceptron (MLP) classification. The K-means clustering algorithm is engaged for grouping analogous nodes into k clusters using the similarity measures such as attack and non-attack, whereas the Neural Network Multi-Layer Perceptron classifies the clustered data into detail categories such as R2L, Probing, DoS, U2R and Normal. …”
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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.