Search Results - (( using function method algorithm ) OR ( frequency identification using algorithm ))
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1
Smoothed functional algorithm with norm-limited update vector for identification of continuous-time fractional-order Hammerstein Models
Published 2024“…This article proposes an identification method of continuous-time fractional-order Hammerstein model using smoothed functional algorithm with a norm-limited update vector (NL-SFA). …”
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2
Identification of continuous-time hammerstein system using sine cosine algorithm
Published 2019“…This paper presents the development of identification of continuous-time Hammerstein systems based on Sine Cosine Algorithm (SCA). …”
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3
A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems
Published 2021“…This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. …”
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4
Relevance detection and summarizing strategies identification algorithm using linguistic measures / Seyed Asadollah Abdiesfandani
Published 2016“…The algorithm simulates two important tasks that are frequently used by the human experts to identify summarizing strategies used to produce the summary sentences: 1) sentences relevance identification; and 2) summarizing strategies identification. …”
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5
Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. …”
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6
Real time nonlinear filtered-x lms algorithm for active noise control
Published 2012“…The SEF has been extensively used to model the saturation nonlinearity. A major drawback of using the SEF function lies in its theoretical nature such that for a finite integration limit, the SEF become non-elementary integral and requires infinite series or numerical methods for evaluation. …”
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7
Nonlinear system identification via basis functions based time domain volterra model
Published 2014“…The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA). …”
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8
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods
Published 2024“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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9
Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique
Published 2018“…In this research, it is to apply the Hilbert-Huang transform (HHT) as a method to analyse the pressure transient signal. The HHT is a way to decompose a signal into intrinsic mode functions (IMF). …”
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10
Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods
Published 2024“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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Proceeding Paper -
11
Effective query structuring with ranking using named entity categories for XML retrieval
Published 2016“…Furthermore, it employs Predicates Identification Algorithm (PIA) and Entity Identification Algorithm (EIA) to identify user search intention. …”
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12
Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
Published 2012“…Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function parameters are adjusted using combination of back propagation algorithm with a least square type of method. …”
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13
Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…Moreover, the ability of initialization the hidden nodes parameters using density function and recursive algorithm will help WN-OSELM to perform useful generalization facility and modeling accuracy. …”
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Experimental analysis of racing car chassis for Modal Identification / M. N. Aizat Zainal ...[et al.]
Published 2018“…Results from both FDD and EMA are validated and compared to ensure that FDD result can be further used in OMA methods of analysis. Only two out of four identification algorithms in parametric OMA techniques will be applied, namely Enhanced Frequency Domain Decomposition (EFDD) and Canonical Variant Analysis of Covariance-driven Stochastic Subspace Identification (SSI-CVA) methods. …”
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Optimization of RFID network planning for monitoring railway mechanical defects based on gradient-based Cuckoo search algorithm
Published 2020“…The Gradient-Based Cuckoo Search (GBCS) algorithm was used to achieve the final objective. …”
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MLP and Elman recurrent neural network modelling for the TRMS
Published 2008“…These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). …”
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Proceeding Paper -
18
Structural steel plate damage detection using DFT spectral energy and artificial neural network
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Pipeline condition assessment by instantaneous frequency response over hydroinformatics based technique—An experimental and field analysis
Published 2021“…The results showed that, although with a low ratio of signal-to-noise, the proposed method could be used as an automatic selector for Intrinsic Mode Function (IMF). …”
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Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei
Published 2021“…Specifically, the back-propagation (BP) network was employed in the supervised learning method, and the FRF changes in a selected frequency interval at several measurement points were used as the input of the network. …”
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