Search Results - (( using sigma methods algorithm ) OR ( java adaptation optimization algorithm ))

Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

Sigma point-based fastslam: Solution to slam problem by Zafizal, Z., Saifudin, Razali, Mohd Razali, Daud, Hamzah, Ahmad

“…The sigma point transformation is used to estimate robot poses in conjunction with generic particle filter used in standard FastSLAM framework. …”
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An improved Pi-Sigma neural network using error feedback for time series prediction by Akram, Urooj

“…This study proposed a model called improved Pi-Sigma Neural Network using Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback. …”
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A modified weight optimisation for higher-order neural network in time series prediction by Husaini, Noor Aida

“…Since its discovery, the CS has been used extensively. However, these methods fixed the parameter values which essential for adjusting the weights. …”
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Under voltage load shedding scheme using meta-heuristic optimization methods [Meta-heurystyczne metody optymalizacyjne wykorzystywane do szybkiego pozbywania si? obci??enia sieci] by Verayiah R., Mohamed A., Shareef H., Abidin I.Z.

Visual Simultaneous Localization and Mapping Using Direct-Based Method for Unmanned Aerial Vehicle (UAV) by M. H., Badzli, N., Mohamad Yatim, A., Jamaludin, Z., Mohd Noh, M. I., Idris, N. A., Sulaiman, Nur Aqilah, Othman

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Adaptive GRNN for the modelling of dynamic plants by Yusof, Rubiyah, Khalid, Marzuki, Teo, Lian Seng

“…It possesses several distinguished features compared to the original GRNN proposed by Specht [1], such as flexible pattern nodes add-in and delete-off mechanism, dynamic initial sigma assignment using non-statistical method, automatic target adjustment and sigma tuning. …”
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Tchebichef moment based restoration of Gaussian blurred images by Kumar, A., Paramesran, R., Lim, C.-L., Dass, S.C.

“…The estimated blur parameters from the proposed method are used in the split Bregman-based image restoration algorithm. …”
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Investigation of load variant under power distribution network reconfiguration using EPSO algorithm by Sulaima, Mohamad Fani, Wong, Kok Loong, Bohari, Zul Hasrizal, Mohd Nasir, Mohamad Na'im

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Performance of Closed Newton-Cotes methods and alternative extended Simpson’s Rule method for solving complex integration functions / Nurul Fatihah Kamaruddin by Kamaruddin, Nurul Fatihah

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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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QOE measurement model in wi-fi networks: Analysis of socioeconomics influences in Indonesia by Ruliyanta, Ahmad, Mohd Riduan, Awang Md Isa, Azmi

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Vibration analysis for early detection of bearing failures by Gam, Kheng Shiang

“…The algorithm's analysis results were consistent with the physical inspection of disassembled bearings. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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