Search alternatives:
modeling algorithm » scheduling algorithm (Expand Search)
method algorithm » mould algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
tracking » training (Expand Search), taking (Expand Search)
element » elements (Expand Search)
modeling algorithm » scheduling algorithm (Expand Search)
method algorithm » mould algorithm (Expand Search)
data modeling » data models (Expand Search), spatial modeling (Expand Search)
tracking » training (Expand Search), taking (Expand Search)
element » elements (Expand Search)
-
21
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
Get full text
Get full text
Get full text
masterThesis -
22
-
23
-
24
Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
Get full text
article -
25
DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data
Published 2022“…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
Get full text
Get full text
Get full text
masterThesis -
26
Resources Allocation for Drones Tracking Utilizing Agent-Based Proximity Policy Optimization
Published 2023“…This paper presents a reinforcement learning agent-based model that works by incorporating the MESA environment with the Stone Soup radar systems simulator. …”
Get full text
-
27
A Novel Internal Model Control Scheme for Adaptive Tracking of Nonlinear Dynamic Plants
Published 2006“…The U-model utilizes only past data for plant modelling and standard root solving algorithm for control law formulation. …”
Get full text
Get full text
article -
28
-
29
Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plants
Published 2006“…The U-model is utilized to design an adaptive inverse controller by using a simple root-solving algorithm of Newton-Raphson. The synergy of U-model with AIC structure has provided an effective and straight forward method for adaptive tracking of nonlinear plants. …”
Get full text
Get full text
article -
30
Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
Published 2024“…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
Get full text
-
31
Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization
Published 2024“…This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. …”
Get full text
-
32
-
33
Efficiency Optimization of a DSP-Based Standalone PV System Using Fuzzy Logic and Dual-MPPT Control
Published 2012“…The second MPPT algorithm controls the power converter between the PV panel and the load and implements a new fuzzy-logic (FLC)-based perturb and observe (P&O) scheme to keep the system power operating point at its maximum. …”
Get full text
article -
34
Learn while Tracking
Published 2007“…A simple non-iterative digital controller is proposed for the uniform output tracking problem of multi-input multi-output discrete-time varying linear systems with arbitrary relative degree. …”
Get full text
Get full text
Get full text
Get full text
conferenceObject -
35
-
36
Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
-
37
-
38
-
39
An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
Published 2021Subjects: -
40
Using genetic algorithms to optimize software quality estimation models
Published 2004“…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …”
Get full text
Get full text
Get full text
masterThesis