Search Results - ((((linear algorithm) OR (((matching algorithm) OR (bees algorithm))))) OR (learning algorithm))
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An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2013“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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An approach to improve functional link neural network training using modified artificial bee colony for classification task
Published 2012“…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. …”
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4
Stereo matching algorithm using deep learning and edge-preserving filter for machine vision
Published 2024“…This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. …”
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Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman
Published 2017“…This project used Artificial Bee Colony Algorithms (ABC) by adapting the food foraging behaviour of bee in honey bee and find a suitable house for home buyer based on their requirement. …”
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Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
Published 2017“…Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. …”
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Static hand sign recognition using linear projection methods
Published 2000“…Successful shape matching algorithms/ methods has a high potential for a wide variety of practical applications. …”
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Proceeding Paper -
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Static hand sign recognition using linear projection methods
Published 2009“…Successful shape matching algorithms/ methods has a high potential for a wide variety of practical applications. …”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
Published 2014“…Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. …”
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Application of the bees algorithm for constrained mechanical design optimisation problem
Published 2019“…Nowadays, many optimisation algorithms have been introduced due to the advancement of technology such as Teaching Learning Based Optimisation (TLBO), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and the Bees Algorithm. …”
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Functional link neural network with modified bee-firefly learning algorithm for classification task
Published 2016“…This work proposed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning algorithm. …”
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Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection
Published 1998“…These algorithms could not compensate for the change or variation on the inspected parts dynamically. …”
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Subjects:Conference paper -
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Indoor comfort and energy consumption optimization using an inertia weight artificial bee colony algorithm
Published 2022“…Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. …”
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Indoor comfort and energy consumption optimization using an inertia weight artificial bee colony algorithm
Published 2022“…Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. …”
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Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
Published 2016“…This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combination of extreme learning machine (ELM) and artificial bee colony optimization (ABC) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. …”
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An improved bees algorithm local search mechanism for numerical dataset
Published 2015“…Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. …”
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Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm
Published 2022“…It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. …”
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A quick gbest guided artificial bee colony algorithm for stock market prices prediction
Published 2018“…The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. …”
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