Search Results - (( variable identification method algorithm ) OR ( java application testing algorithm ))
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. …”
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Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models
Published 2024“…This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). …”
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RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Final Year Project -
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Journal -
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Robust Estimation Methods And Outlier Detection In Mediation Models
Published 2010“…We suggest an alternative method for identification of high leverage points in the mediation model. …”
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Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
Published 2018“…The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. …”
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Modelling of cupping suction system based on system identification method
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Conference or Workshop Item -
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Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure
Published 2016“…One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. …”
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Implementation of (AES) Advanced Encryption Standard algorithm in communication application
Published 2014“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Undergraduates Project Papers -
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Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model
Published 2001“…The method of selection of the input variables, the number of rules, and the learning rate are briefly discussed. …”
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Classification System for Heart Disease Using Bayesian Classifier
Published 2007“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. In this system a Bayesian algorithm was used in order to implement the system. …”
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Robust and adaptive control with application to mechanical systems
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Working Paper -
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Model input and structure selection in multivariable dynamic modeling of batch distillation column pilot plant / Ilham Rustam
Published 2015“…Comparison results have shown that the pre-screening method has an essential role in determining an effective process representation especially in real-time multivariable identification framework where a priori knowledge is not available and would help in resultant model generalization performance as opposed to simply using all available model input variables. …”
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Modeling of flood water level prediction using NNARX / Fazlina Ahmat Ruslan
Published 2015“…Comparison results have shown that the pre-screening method has an essential role in determining an effective process representation especially in real-time multivariable identification framework where a priori knowledge is not available and would help in resultant model generalization performance as opposed to simply using all available model input variables. …”
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