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Year: 2011, Volume: 11, Number: 2
... 2011 A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks Choon Ki AHN Faculty of the Department of ... Engineering, Seoul National University of Science& Technology 172 Gongneung 2-dong, Nowon-gu, Seoul 139-743, Korea hironaka ... the states of neural Consider the following multilayer Hopfield neural network networks using measurements and then use the estimated with ...
Year: 2018, Volume: 18, Number: 1
... for action support vector machines (SVM), and neural networks (NN) recognition using an acceleration sensor ... The data for the techniques and aggregates their outputs by using fuzzy acceleration sensor of the smartphone has been collected ... assignment of optimal confidence levels of the classifiers. The network, decision tree, and logistic regression for action ...
Year: 2007, Volume: 7, Issue: 1
... ), 2007 Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System Igor ASTROV1, Sergei TATARLY2, Svetlana ... system under measurement constraints is described. In this neural networks, simulation. system, the feedback controller is ... at the slow rate, while the feedforward controller is updated at the fast rate with a desired sequence. ...
Year: 2023, Volume: 23, Number: 3
... aims to make the system information by building a graph neural network of two modalities and then perform inter-modal fusion ... video. The next cross-modal interaction module fuses the neural networks [20-21] or based on dual-stream networks features ... Volume 23, Number 3, 2023 Video Moment Localization Network Based on Text Multi-semantic Clues Guidance Guangli WU, ...
95. Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features
Year: 2024, Volume: 24, Number: 1
... fusion [7]. In identification and deep convolutional neural networks (CNNs) the field of biometric feature ... techniques include sensor-level fusion, feature-level fusion, wavelet transform networks (WTN) for face and iris match-score-level fusion ... irises, as well as deep convolutional neural networks neural network (CNN) and support vector classifier (SVC ...
Year: 2011, Volume: 11, Number: 4
... dynamics, optimized for implementations. digital implementation of Spiking Neural Networks. Its 1 A good compromise between computational ... and architecture is structured in two major blocks, a datapath and repertoire of spiking is the neuron model proposed ... 4, 2011 A Neuron Model for FPGA Spiking Neuronal Network Implementation Liviu ŢIGĂERU, Gabriel BONTEANU Gheorghe Asachi Technical ...
Year: 2019, Volume: 19, Number: 3
... , 2019 Spectral Subband Centroid Energy Vectors Algorithm and Artificial Neural Networks for Acoustic Emission Pattern Classification Marcus Tulius Barros ... ,2, Edson Guedes da COSTA1, Tarso Vilela FERREIRA1,3, André Dantas GERMANO1 1 Post-graduation Program ... proposes the use of artificial about a new artificial neural network training algorithm was neural networks (ANN) for ...
Year: 2018, Volume: 18, Number: 4
... UAV. In wind tunnel method, study, Artificial Neural Network (ANN) based objective function is optimized ... profile using Genetic Algorithm (GA) intelligence, neural networks, optimization. enhanced with an Artificial Neural Network ... ANN) and in his another study; he adapted this work with simulated I. INTRODUCTION annealing for better ...
Year: 2023, Volume: 23, Number: 4
... learning models — Decision Tree (DT), Artificial Neural resistant to adversarial attacks. A random subspace Network ... detection. Adaptive adversaries who comprehend the infrastructure of computer networks. The administrators of underlying principles of ML techniques ... Engineering Volume 23, Number 4, 2023 A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace ...
Year: 2022, Volume: 22, Number: 3
... USD depending on their age range, monitoring, fuzzy neural networks, neurocontrollers, virtual prototyping. ethnicity, ... , health condition, among others. These expenses are usually higher for young people (children and I. ... High-Order to represent glucose transit through the stomach and Neural Network. This neural network structure allows us to intestine ...
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