Search Results - (( program implementation learning algorithm ) OR ( parameters derivation method algorithm ))

Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

“…In the algorithm development a step-by-step example of the algorithm implementation is presented and then successfully implemented in Lego Mindstorm obstacle avoiding mobile robot as a proof of concept implementation of the hybrid AI algorithm. …”
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Virtual reality in algorithm programming course: practicality and implications for college students by Dewi, Ika Parma, Ambiyar, Mursyida, Lativa, Effendi, Hansi, Giatman, Muhammad, Efrizon, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

“…The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. …”
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An application of simulated Kalman filter optimization algorithm for parameter tuning in proportional-integral-derivative controllers for automatic voltage regulator system by Badaruddin Muhammad, Dwi Pebrianti, Normaniha Abdul Ghani, Nor Hidayati Abdul Aziz, Nor Azlina Ab Aziz, Mohd Saberi Mohamad, Mohd Ibrahim Shapiai, Zuwairie Ibrahim

“…Compared to another well-established optimizer, such as particle swarm optimization (PSO), the SKF algorithm is a relatively new optimizer and most importantly, the SKF algorithm has not been applied to parameter tuning of PID controller. …”
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An application of simulated Kalman filter optimization algorithm for parameter tuning in proportional-integral-derivative controllers for automatic voltage regulator system by Badaruddin, Muhammad, Pebrianti, Dwi, Normaniha, Abd Ghani, Nor Hidayati, Abdul Aziz, Nor Azlina, Ab Aziz, Mohd Saberi, Mohamad, Mohd Ibrahim, Shapiai, Zuwairie, Ibrahim

“…The findings suggest that the SKF algorithm has promising potentials to be a good tuning method in PID controller.…”
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

A new domain specific scripting language for automated machine learning pipeline by Masrom, S., Rahman, A.S.A., Omar, N., Baharun, N.

“…However, in respond to the implementation difficulty, there exists a limited software tool that support easy implementation for automated machine learning based on Genetic Programming. …”
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Rapid software framework for the implementation of machine learning classification models by Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.

“…However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. …”
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Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

“…From this derivation, we construct the CI for the concentration parameter. …”
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Parameter-driven count time series models / Nawwal Ahmad Bukhari by Nawwal , Ahmad Bukhari

“…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

“…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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Weighting method for modal parameter based damage detection algorithms by Fayyadh, M.M., Abdul Razak, H.

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An Approach to Derive Parametric L-System Using Genetic Algorithm by Farooq, H., Zakaria, M.N., Hassan, M.F., Sulaiman, S.

“…The higher level of GA deals with the evolution of symbols and lower level deals with the evolution of numerical parameters. Initial results derived from the approach are very promising, which shows that complicated branching structures can be easily derived by the multilayered architecture of GA. …”
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

“…Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. …”
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A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier by Noormadinah Allias, Megat NorulAzmi Megat Mohamed Noor, Mohd. Nazri Ismail, Kim de Silva, (UniKL MIIT)

“…A performance of anti-spam filter not only depends on the number of features and types of classifier that are used, but it also depends on the other parameter settings. Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. …”

Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

“…Another problem is estimating the covariance matrix of the parameter estimators, since the existing methods involve derivative of the hazard function of the model’s error terms. …”
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Pendulum-like algorithm as a local search technique by Abed I.A., Koh S.P., Sahari K.S.M., Tiong S.K., Younis H.A.-K., Abed A.A.

Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network by Kang, Miew How

“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…The classification accuracy obtained from the CST method is compared to other selected classification methods such as Value Difference Metric (VDM), Pre-Category Feature Importance (PCF), Cross-Category Feature Importance (CCF), Instance-Based Algorithm (IB4), Decision Tree Algorithms such as Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5), Rough Set methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) and Neural Network methods such as the Multilayer method.…”
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BBO algorithm-based tuning of PID controller for speed control of synchronous machine by Kasilingam G., Pasupuleti J.

An interactive C++ programming courseware (SIFOO) / Mazliana Hasnan … [et al.] by Hasnan, Mazliana, Othman, Zainab, Mohamad, Norizan, Abdullah, Zamlina

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