Search Results - (( program navigation using algorithm ) OR ( parallel optimization learning algorithm ))
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Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi
Published 2016“…Performance indices such as workspace, dexterity and stiffness, of the parallel manipulator are studied. The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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Student Project -
2
Implementation of Autonomous Vehicle Navigation Algorithms Using Event-Driven Programming
Published 2012“…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. …”
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Conference or Workshop Item -
3
MAZE ROBOT: APPLYING AUTONOMOUS VEHICLE NAVIGATION ALGORITHM WITH EVENT-DRIVEN PROGRAMMING
Published 2011“…Event-driven programming method was applied in producing the maze navigation algorithm for the robot.…”
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Final Year Project -
4
Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. …”
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Article -
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Implementation of autonomous vehicle navigation algorithms using event-driven programming
Published 2012“…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. …”
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Conference or Workshop Item -
6
Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi
Published 2015“…Performance indices such as workspace, dexterity and stiffness, of the parallel manipulator are studied. The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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Thesis -
7
PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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Thesis -
8
Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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Article -
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A parallel ensemble learning model for fault detection and diagnosis of industrial machinery
Published 2023“…However, the gradient descent optimization method that is commonly used in deep learning suffers from several limitations, such as high computational cost and local sub-optimal solutions. …”
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Article -
10
PMT: opposition-based learning technique for enhancing meta-heuristic performance
Published 2019“…Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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Article -
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Vision-based robot indoor navigation
Published 2022“…This project aims to develop a vision-based navigation robot using OpenCV and C++ programming language in an indoor environment. …”
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Final Year Project / Dissertation / Thesis -
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Implementation of an autonomous mobile robot navigation algorithm using C language
Published 2009“…After that, implement the navigation algorithm on the mobile robot and without using the external sensor for navigation of the mobile robot to reach the specified point. …”
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Final Year Project -
13
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
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Thesis -
14
Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
Published 2019“…In this paper, the particle swan optimization algorithm (PSO) was used to obtain an optimal set of initial parameters for Reduce Kernel FLN (RK-FLN), thus, creating an optimal RKFLN classifier named PSO-RKELM. …”
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Conference or Workshop Item -
15
Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Conference or Workshop Item -
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Design Of Robot Motion Planning Algorithm For Wall Following Robot
Published 2006“…Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. …”
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Monograph -
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Indoor Navigation Algorithm For Mobile Robot
Published 2016“…Set of experiments presented to validate our system using MATLAB program. Testing verified that good accuracy, sufficient for navigation, was achieved. …”
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Book Section -
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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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Article -
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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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