Search Results - (( model evaluation from algorithm ) OR ( _ identification based algorithm ))
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1
Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model
Published 2021“…This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. …”
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2
Identification of non-linear dynamic systems using fuzzy system with constrained membership functions
Published 2004“…In this study, the identification performance of the SFS trained by the back-propagation (BP) algorithm forms the basis of comparison when evaluations were made on the performance of the newly proposed CFS models. …”
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3
Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane
Published 2021“…This paper presents the identification of double pendulum overhead crane (DPOC) plant based on the hybrid Multi-Verse Optimizer with Sine Cosine Algorithm (HMVOSCA) using the continuous-time Hammerstein model. …”
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4
System identification using Extended Kalman Filter
Published 2017“…System identification is getting more intensive from researcher to develop an algorithm with work efficiently and more accurate. …”
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Student Project -
5
A Robust Firearm Identification Algorithm of Forensic Ballistics Specimens
Published 2017“…A total of 250 simulated noisy images collected from five different pistols of the same make, model and caliber are used to evaluate the robustness of the proposed algorithm. …”
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6
A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. …”
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7
Sketch-based 3D modeling of symmetric objects from wireframe sketches on paper
Published 2016“…The results of the validation show a complete similarity between each original face and the output from our algorithm. Furthermore, the algorithm is also evaluated by human experts and shows 100% correctness.…”
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8
Opposition- based simulated kalman filters and their application in system identification
Published 2017“…Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. …”
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9
Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)
Published 2024“…System identification is a method to build a model for a dynamic system from the experimental data. …”
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Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin
Published 2014“…The algorithm searches the solution space by selecting various model structures and evaluating its fitness. …”
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11
Real time nonlinear filtered-x lms algorithm for active noise control
Published 2012“…The ANC architecture implemented is a single channel internal model control (IMC) based feedback ANC system. Simulation and experimental results have shown that the developed THF-NLFXLMS achieves additional noise reduction of 19% from that being achieved by the linear FXLMS algorithm.…”
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12
Application of system identification method coupled with evolutionary algorithms for the optimization of power consumption in a pem fuel cell propulsion system / Suhadiyana Hanapi
Published 2018“…This thesis makes a number of key contributions to the advancement of fuel cell vehicle design within two main research areas; powertrain system design based on quality energy, and optimization system based on biology based algorithms. …”
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Book Section -
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Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)
Published 2018“…System identification is a method to build a model for a dynamic system from the experimental data. …”
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14
An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif
Published 2016“…Immune based algorithm is part of bio-inspired algorithms elicits theories which can act as an inspiration for computer-based solutions. …”
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Research Reports -
15
Relevance detection and summarizing strategies identification algorithm using linguistic measures / Seyed Asadollah Abdiesfandani
Published 2016“…To evaluate the algorithm, we conducted two experiments. …”
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16
Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…However, the developed algorithms only consider the selected features from a peak model based on the understanding of the EEG signals characteristics. …”
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17
Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data
Published 2020“…Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. …”
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A Hybrid Artificial Intelligence Model for Detecting Keratoconus
Published 2023“…This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. …”
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Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga
Published 2022“…First, to investigate the methods used to address limitations associated with microbiome-based datasets in colorectal cancer identification using deep neural network algorithms. …”
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20
Abnormal data detection model based on autoencoder and random forest algorithm: camera sensor data in autonomous driving systems
Published 2025“…Sensor failure, environmental changes, or bad weather can lead to the emergence of abnormal data, which can affect the decision-making process and may have disastrous consequences. Based on the above problems, this study addresses this challenge by proposing a hybrid anomaly detection model (called CAE-RF) that combines convolutional autoencoders and random forest algorithms to achieve efficient and accurate identification of abnormal data patterns to improve the safety of autonomous driving systems. …”
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