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About 10,000 papers for “Adaptive algorithm”

Adaptive algorithm

Sara Pollock, Collaborators Michael Holst, Yunrong Zhu et al. · 2013

Cited by 0

Adaptive Cone Algorithm

Purba Daru Kusuma, Meta Kallista · International Journal on Advanced Science Engineering and Information Technology · 2023

This study was conducted to promote a new adaptive cone algorithm (ACA) algorithm. ACA is a metaheuristic technique based on swarm intelligence. ACA contains three steps. Each agent moves closer to the global reference in the first step. Then, each agent searches for a better solution around the current solution in the second step. The global reference searches for better solutions around it in the third step. This algorithm is named cone because the local space size declines linearly during the iterative process. ACA introduces a new adaptability model to improve the exploration strategy when a better solution cannot be achieved. It is conducted by enlarging the local solution space. ACA is challenged to find the final solution for theoretical and practical problems. The 23 functions are chosen as theoretical optimization problems. The portfolio optimization problem is selected as the practical problem. ACA is compared with five algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), marine predator optimization (MPA), average subtraction-based optimizer (ASBO), and pelican optimization algorithm (POA). The result shows that ACA is competitive in finding the optimal solution for 23 functions and outperforms all sparing algorithms in achieving the highest total capital gain in tackling the portfolio optimization problem. ACA is superior to PSO, GWO, MPA, ASBO, and POA in solving 20, 11, 13, 4, and 21 functions, respectively. In the future, ACA can be implemented in solving various practical optimization problems.

Cited by 2Open access

Adaptive Thinning Algorithm:

Theije, P.A.M. de · TNO Repository · 2002

A new adaptive method is presented to display large amounts of data on, for example, a computer screen. The algorithm reduces a set of N samples to a single value, using the statistics of the background and cormparing the true peak value in the set of N samples to the expected peak value of this background, taking into account a possible global trend in the data. The new algorithm clearly performs better than conventional ones in terms of signal-to-noise ratio. The algorithm has been successfully applied to display low-frequency active sonar data, used for Anti-submarine Warfare experiments, where the background followed a Rayleigh distribution. The algorithm is easy to apply, and can be used for data reduction in one or more dimensions and for arbitrary background distributions.

Cited by 0

Fast adaptive esprit algorithm

Roland Badeau, Gaël Richard, Bertrand David · IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 · 2005

The ESPRIT algorithm is a subspace-based high resolution method used in source localization and spectral analysis. It relies on the rotational invariance property of the signal subspace, and provides accurate estimates of the signal parameters. However, its main drawback is a high computational cost. In an adaptive context, some very fast algorithms were proposed to robustly track the variations of the signal subspace. Based on these subspace trackers, we propose a new adaptive implementation of the ESPRIT algorithm, faster than the existing methods

Cited by 21

Adaptive hierarchy genetic algorithm

Dunwei Gong, Xiaoyan Sun, Xijin Guo et al. · 2004

Contraposed with insufficiency of the simple hierarchy genetic algorithm, the concept of individual density is first proposed in this paper to scale the diversity of population. Combined with individual density and evolution time, the adaptive hierarchy genetic algorithm is put forward. Not only can it improve the diversity of the evolution population, but also ensure rapid convergence. Application examples show its validity.

Cited by 0

Adaptive-feedback control algorithm

D. Huang · Physical Review E · 2006

This paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers [Phys. Rev. Lett. 93, 214101 (2004); Phys. Rev. E 71, 037203 (2005); 69, 067201 (2004)], where an adaptive-feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems. This note proves in detail the strictness of this algorithm from the viewpoint of mathematics, and gives some interesting remarks for its potential applications to chaos control & synchronization. In addition, a significant comment on synchronization-based parameter estimation is given, which shows some techniques proposed in literature less strict and ineffective in some cases.

Cited by 102

Finger motion adaptive algorithm

Melda Ulusoy · Figshare · 2015

Raw dataset obtained from experiments with sighted people using the finger motion adaptive algorithm. Texts T1 and T2 are used in the experiments performed with blind subjects.

Cited by 0Open access

AeFd adaptive expansion algorithm.

Yufeng Fan (734864), Haotai Sun (18078268) · Figshare · 2024

<div><p>In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability.</p></div>

Cited by 0Open access

Adaptive parallel tempering algorithm

Błażej Miasojedow, Éric Moulines, Matti Vihola · arXiv (Cornell University) · 2012

Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algorithms often fail. The mixing properties of the sampler depend strongly on the choice of tuning parameters, such as the temperature schedule and the proposal distribution used for local exploration. We propose an adaptive algorithm which tunes both the temperature schedule and the parameters of the random-walk Metropolis kernel automatically. We prove the convergence of the adaptation and a strong law of large numbers for the algorithm. We illustrate the performance of our method with examples. Our empirical findings indicate that the algorithm can cope well with different kind of scenarios without prior tuning.

Cited by 9Open access

Adaptive lighting algorithm experiment

Xun Zhang, Yuehua Zhou · 2017

The development of lighting technology and the promotion of energy crisis, focusing on Lighting energy saving and light environmental quality has become an important trend of adaptive intelligent lighting control technology. In order to make efficient use of natural light, reduce the energy loss of artificial lighting, improve the quality of indoor lighting, put forward two concepts: wall reflection increment coefficient and point illumination on working face. Firstly, research the interior space of the office. Then, use DIALux software to simulate the same size of the space. Next, measure the illumination of different relationships. It provides a scientific basis for the algorithm to the intelligent lighting adaptive control that studying the reflection reflectance of the wall reflects the reflection increment of the indoor illumination, and the line light source generated the illumination relationship of the various points on working face.

Cited by 2

Adaptive hierarchy genetic algorithm

Dunwei Gong, Xiaoyan Sun, Win Guo et al. · 2005

Contraposed with insufficiency of the simple hierarchy genetic algorithm, the concept of individual density is first proposed in this paper to scale the diversity of population. Combined with individual density and evolution time, the adaptive hierarchy genetic algorithm is put forward. Not only can it improve the diversity of the evolution population, but also ensure rapid convergence. Application examples show its validity.

Cited by 5

Adaptive Group Testing Algorithm

K. Yu. Gorbunov · Journal of Communications Technology and Electronics · 2025

An algorithm is described for group testing under the conditions when, in a specified set of cardinality $$n$$ , there is an unknown amount of $$d > 0$$ of defective elements and it is necessary to identify them with a sequence of tests. Each next test can take into account the results of the previous ones. The upper estimate for the number of tests in our algorithm improves the known value. The algorithm runtime is the minimum possible: it is of the order of the number of tests.

Cited by 0

Finger motion adaptive algorithm

Melda Ulusoy · Figshare · 2015

Raw dataset obtained from experiments with sighted people using the finger motion adaptive algorithm. Texts T1 and T2 are used in the experiments performed with blind subjects.

Cited by 0Open access

COMB ADAPTIVE FILTERING ALGORITHM

A. Yu. Linovich, V.S. Litvinova, M. D. Korolev · Vestnik of Ryazan State Radio Engineering University · 2021

The problem of multipath channel frequency response equalization in a receiver is considered. The aim is to develop an algorithm of comb adaptive filtering, which makes possible, on the one hand, to provide high rate of multirate receiver system adaptation, and on the other hand, to reduce computational complexities of real-time processing. The robustness analysis of the suggested algorithm is carried out. Two variants of comb adaptive filter are studied. For the second one a fast modification is proposed. On the assumption of multichannel communication system equalizer realization the developed algorithm is able to provide the advantage in rate almost by a factor of ten and at the same time to reduce computational complexities by a factor of three as compared with the least-mean-square algorithm as the experimental results demonstrate.

Cited by 1

Finger motion adaptive algorithm

Melda Ulusoy · Figshare · 2015

Raw dataset obtained from experiments with sighted people using the finger motion adaptive algorithm. Videos captured during mechanical experiments and Braille eperiments. Texts T1 and T2 are used in the experiments performed with blind subjects.

Cited by 2Open access