Search Results - (( developing function learning algorithm ) OR ( learning active learning algorithm ))

Acceleration Strategies For The Backpropagation Neural Network Learning Algorithm by Zainuddin, Zarita

“…In this thesis, factors that govern the learning speed of the backpropagation algorithm are investigated and mathematically analyzed in order to develop strategies to improve the performance of this neural network learning algorithm. …”
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Particle swarm optimization for neural network learning enhancement by Abdull Hamed, Haza Nuzly

“…In Backpropagation Neural Network (BPNN), there are many elements to be considered such as the number of input, hidden and output nodes, learning rate, momentum rate, bias, minimum error and activation/transfer functions. …”
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Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…These algorithms have widely been used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. …”
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Jogging activity recognition using k-NN algorithm by Afifah Ismail

“…The k-NN algorithm is a simple and easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. …”
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Unified neural network controller of series active power filter for power quality problems mitigation by Ghazanfarpour, Behzad

“…First, Widrow-Hoff algorithm is examined and its constant learning rate is modified by adding an adaptive learning rule to change the learning rate value. …”
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Forecasting of photovoltaic output using hybrid particle swarm optimization-artificial neural network model / Muhamad Faizol Adli Abdullah by Abdullah, Muhamad Faizol Adli

“…These entire elements will affect the speed of natural network learning. In this study, the optimization algorithm, PSO is chosen and applied in feedforward neural network to enhance the learning process. …”
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An intelligent framework for modelling and active vibration control of flexible structures by Mohd. Hashim, Siti Zaiton

“…The work is further extended to developing and integrating the idea of active control of flexible structures into an interactive learning environment. …”
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A hybrid technique of deep learning neural networks with finite difference method for higher order fractional Volterra-Fredholm integro-differential equations with φ-Caputo operato... by Alsa’Di, Kawthar, Nik Long, Nik Mohd Asri

“…This technique uses the Adaptive Moment Estimation Method (Adam) as an optimization algorithm with feed-forward deep learning to minimize the error function and training the model using five layers with different activation functions. …”
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Forensic language of property theft genre based on mathematical formulae and machine learning algorithms / Hana' Abd Razak by Abd Razak, Hana'

“…Convolution Neural Network (CNN) using deep learning algorithm is chosen in identifying frequency of movement and execution time of housebreaking crime. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…This learning algorithm represents an automatic generation of membership functions and rules from the data. …”
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E-Handrawn Calculator by Mohamad, Syamimi

“…The purpose of this project is to demonstrate an application of back-propagation network (comparison of training their algorithms and transfer function) in order to developing e-Hand-Drawn Calculator. …”
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Extreme Learning Machine neural networks for multi-agent system in power generation by Yaw C.T., Wong S.Y., Yap K.S., Tan C.H.

“…Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. …”

PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

“…Particularly, GA is utilized to determine the optimal number of hidden layers, number of neurons in each hidden layer, type of training algorithm, type of activation function of hidden and output neurons, initial weight, learning rate, momentum term, and epoch size of a multilayer feed-forward ANN. …”
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ECG peak recognition using Artificial Neural Network / Sharifah Saliha Syed Bahrom and Leong Jenn Hwai. by Syed Bahrom, Sharifah Saliha, Leong, Jenn Hwai

“…The MLP network is trained with two different types of learning algorithms, namely the Levenberg Marquardt (LM) and the Bayesian Regularization (BR) and with different numbers of hidden neurons and transfer functions. …”
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Machine learning application in predicting anterior cruciate ligament injury among basketball players by Longfei, Guo

“…A one-year follow-up was conducted to monitor ACL injury, identifying n=11 injured players. Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR)—were developed to predict ACL injury. …”
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Application of deep learning technique to predict downhole pressure differential in eccentric annulus of ultra-deep well by Krishna, S., Ridha, S., Ilyas, S.U., Campbell, S., Bhan, U., Bataee, M.

“…The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Kerasâ��s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. …”
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Polynomial neural network for solving Caputo-conformable fractional Volterra–Fredholm integro-differential equation with three-point non-local boundary conditions by Alsa’di, Kawthar, Nik Long, Nik Mohd Asri, Senu, Norazak, Eshkuvatov, Z.K.

“…A hybrid technique, combining a polynomial neural network (PNN) with an extreme learning machine algorithm without using any activation functions, is developed. …”
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Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah by Abdullah, Nur Raudzah

“…Existing researches on air pollution forecasting used a variety of machine learning algorithm. One of the popular algorithms used to forecast the air pollution is Artificial Neural Network (ANN). …”
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Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir by Mahadhir, Khairul Azmi

“…In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). …”
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