Search Results - (( program implementation learning algorithm ) OR ( parallel identification using algorithm ))
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Tile-Level Parallelism For H.264/Avc Codec Using Parallel Domain Decomposition Algorithm On Shared Memory Architecture
Published 2015“…By assuming that parallel architectures are forming the vast majority of computing nodes in digital devises, proposing inherently-parallel algorithms are no more an overstatement. …”
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2
Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
Published 2022“…Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. …”
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3
Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…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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4
Parallel implementation of genomic sequences classification using modified gabor wavelet transform on multicore systems
Published 2012“…Single Program Multiple Data (SPMD) parallel paradigm will be used in the parallel implementation. …”
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5
Virtual reality in algorithm programming course: practicality and implications for college students
Published 2024“…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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Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi
Published 2015“…A parallel manipulator is a closed loop mechanism which consists of a moving platform that is connected to a fixed base by at least two kinematic chains in parallel. …”
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7
Efficient hardware design for palm-dorsa vein image enhancement
Published 2018“…Vein biometric system uses the pattern of veins in the human body as a unique identification. …”
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8
Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Conference paper -
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A comparison study between integrated OBFARX-NN and OBF-NN for modeling of nonlinear systems in extended regions of operation
Published 2014“…A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. …”
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10
Integrated OBF-NN models for extrapolation enhancement in conventional neural networks for nonlinear systems
Published 2011“…A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters (OBF) and a nonlinear feedforward (MLP) NN model is used and applied to the nonlinear Van de Vusse reactor. …”
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Small-scale helicopter system identification model using recurrent neural networks
Published 2010“…The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. …”
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A new domain specific scripting language for automated machine learning pipeline
Published 2019“…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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14
Rapid software framework for the implementation of machine learning classification models
Published 2021“…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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15
A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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16
A parallel-model speech emotion recognition network based on feature clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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17
Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms
Published 2008“…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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18
Wavelet network based online sequential extreme learning machine for dynamic system modeling
Published 2013“…The proposed model used as system identification for nonlinear dynamic systems. …”
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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network
Published 2016“…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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20
Case Slicing Technique for Feature Selection
Published 2004“…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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