Search Results - (( network implementation learning algorithm ) OR ( parameters deviations methods algorithm ))
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1
Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…The statistical analysis value (mean) was taken from the parameter deviation index to see how much our proposed algorithm has improved. …”
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2
Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…From the reviews, it is evident that autonomous system is set to handle finite number of encountered states using finite sequences of actions. In order to learn the optimized states-action policy the self-learning algorithm is developed using hybrid AI algorithm by combining unsupervised weightless neural network, which employs AUTOWiSARD and reinforcement learning algorithm, which employs Q-learning. …”
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3
Implementation of machine learning algorithm in preventing network congestion
Published 2023text::Final Year Project -
4
Training functional link neural network with ant lion optimizer
Published 2020“…This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. …”
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5
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm
Published 2023“…Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States…”
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6
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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7
A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems
Published 2021“…The results showed that the proposed method was efficient in identifying both the Hammerstein model subsystems in terms of the quadratic output estimation error and parameter deviation index. …”
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8
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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9
Hybrid bat algorithm-artificial neural network for modeling operating photovoltaic module temperature: article / Noor Rasyidah Hussin
Published 2014“…In other words, the implemented bat algorithm in neural network structure is to get global optimization in order to minimize mean absolute percentage error, MAPE. …”
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10
PID Parameters Improvement for AGC in Three Parallel-Connected Power Systems
Published 2016“…The optimal parameters of the PID scheme optimized by the proposed MS algorithm are compared with that one’s obtained by GA algorithm, and the proposed method has proven that its performance is more efficient and improved as well. …”
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11
Multi-Backpropagation network
Published 2002“…The learning mechanism for Neural Network is its learning algorithm. …”
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12
Statistical approach on grading: mixture modeling
Published 2006“…The problem lies in estimating the posterior density of the parameters which is analytically intractable. A solution to this problem is using the Markov Chain Monte Carlo method namely Gibbs sampler algorithm. …”
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13
Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm
Published 2018“…In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. …”
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14
Anomaly detection in network traffic using machine learning
Published 2026“…In this paper, an anomaly detection method is proposed using machine learning (ML) techniques. The study objective is to analyze the effectiveness and reliability of implementing machine learning techniques in identifying anomalies in network traffic. …”
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15
Computationally efficient sequential learning algorithms for direct link resource-allocating networks
Published 2005“…Computationally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). …”
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16
One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network
Published 2012“…Hence, this indicates that Invasive Weed Optimization could be implemented as a new learning algorithm for an Artificial Neural Network.…”
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17
Hybrid bat algorithm hybrid-artificial neural network for modeling operating photovoltaic module temperature / Noor Rasyidah Hussin
Published 2014“…In other words, the implemented bat algorithm in neural network structure is to get global optimization in order to minimize mean absolute percentage error, MAPE. …”
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18
Feature selection in intrusion detection, state of the art: A review
Published 2016“…By removing these irrelevant and redundant features accuracy of the learning algorithms can be increased. In this paper implementation of different feature selection techniques have been reviewed. …”
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19
Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks
Published 2021“…Conventionally back-propagation learning algorithm also termed as (BP-MLP) is used. …”
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20
CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS
Published 2017“…Therefore, this project introduces three learning algorithms which are Extreme Learning Machine (ELM), Finite Impulse Response Extreme Learning Machine (FIR-ELM) and Discrete Fourier Transform Extreme Learning Machine (DFT-ELM) to improve the bearing fault diagnosis accuracy and shorten the time used to train and test the neural network.…”
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Final Year Project
