Search Results - (( intelligence among process algorithm ) OR ( intelligence based training algorithm ))
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Hybrid intelligent approach for network intrusion detection
Published 2015“…Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. …”
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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review
Published 2022“…While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithmâ��s or methodâ��s aptitude for resolving the provided errors. …”
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Intelligent fault diagnosis for broken rotor bar using wavelet packet signature analysis
Published 2016“…Intelligent methods have concerted on sensing precise failure modes and recommending intelligent maintenance decisions based on the signatures collected through signal processing. …”
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Underwater Image Recognition using Machine Learning
Published 2024“…Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. …”
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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines
Published 2017“…Towards this end, a number of key performance variables, which are commonly measurable on most industrial gas turbine engines, were monitored, and their associated ANNs were trained for healthy conditions. Two back-propagation training algorithms, namely the Levenberg–Marquardt and Bayesian regularization algorithms, and the k-fold cross-validation technique, were employed to train the optimal networks using a training data set. …”
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RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…Science and technology development has the tendency of learning from nature where human also try to develop artificial intelligent by imitating biological neuron network which is popularly termed Artificial Neural Network (ANN). …”
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Pengesanan Kerosakan Bahan Penebat Transformer Dengan Menggunakan Rangkaian Neural Buatan
Published 2006“…Three types of learning algorithm are used in this project to train the MLP network, which are resilient backpropagation, Bayesian regularization and Levenberg-Marquardt. …”
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Real time self-calibration algorithm of pressure sensor for robotic hand glove system
Published 2019“…This study investigates the use of a novel Proposed Self-Calibration Algorithm (PSCA) of multi pressure sensors in real time on robotic hand glove system. …”
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Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems
Published 2024“…The ANNs trained by the optimized DA also achieve higher accuracy than those trained by some other swarm intelligence algorithms. …”
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Integration of dual intelligent algorithms in shunt active power filter
Published 2013“…This paper presents an integration of dual intelligent algorithms: artificial neural network (ANN) based fundamental component extraction algorithm and fuzzy logic based DC-link voltage self-charging algorithm (fuzzy self-charging algorithm), in a three-phase three-wire shunt active power filter (SAPF). …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks
Published 2015“…The efficacy of these models depends upon many factors such as, neural network architecture, type of training algorithm, input training and testing data set and initial values of synaptic weights. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
Published 2004“…The design network is trained by presenting several target machining data that the network must learn according to a learning rule (algorithm). …”
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Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates
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