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Journal articles on the topic 'Adaptive algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Litvinchuk, Yuliia. "Self-adaptive CMA-ES Algorithm." Mathematical and computer modelling. Series: Physical and mathematical sciences 24 (December 5, 2023): 81–90. http://dx.doi.org/10.32626/2308-5878.2023-24.81-90.

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This article will consider one of the self-adaptive algorithms for selecting parameters of complex systems, examples of which are neural networks. Self-adaptive algorithms are algorithms that change their behavior at runtime based on available information and predetermined reward mechanisms. These algorithms are widely used in various fields, including machine learning, optimization, and data compression. The self-adaptiveness of the algorithm in this case will be based on the selection of the number of peaks in the mixture of distributions in the extended CMA-ES algorithm under the condition

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Zhang, Zhaoxia. "Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm." International Journal of Intelligent Information Technologies 20, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijiit.344024.

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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adapt

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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 590–97. http://dx.doi.org/10.1609/aaai.v34i01.5398.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive gr

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Chen, Wei, Binghui Peng, Grant Schoenebeck, and Biaoshuai Tao. "Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization." Journal of Artificial Intelligence Research 74 (May 26, 2022): 303–51. http://dx.doi.org/10.1613/jair.1.12997.

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We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive gr

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O'Malley, Lawrence V. "Adaptive clustering algorithm." IBM Journal of Research and Development 29, no. 1 (1985): 68–72. http://dx.doi.org/10.1147/rd.291.0068.

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Kusuma, Purba Daru, and Meta Kallista. "Adaptive Cone Algorithm." International Journal on Advanced Science, Engineering and Information Technology 13, no. 5 (2023): 1605. http://dx.doi.org/10.18517/ijaseit.13.5.18284.

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Guan, Sihai, Qing Cheng, Yong Zhao, and Bharat Biswal. "Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function." PLOS ONE 16, no. 10 (2021): e0258155. http://dx.doi.org/10.1371/journal.pone.0258155.

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Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based o

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Sarma, Priyanka, and Atowar Ul Islam. "Implementation of Hybrid Adaptive Learning Algorithm for Task Offloading." Indian Journal Of Science And Technology 17, no. 38 (2024): 3929–36. http://dx.doi.org/10.17485/ijst/v17i38.2280.

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Objectives: Offloading tasks in edge computing (EC) plays an important role in optimizing resource utilization and enhancing the system performance. This paper studies various AI-based computation offloading (CO) strategies and proposes a hybrid Adaptive Learning Algorithm, that reduces the latency significantly compared to traditional RL-based on-policy and off-policy algorithms namely Q-Learning (QL) and State Action Reward State Action (SARSA) for CO in an EC environment. The paper evaluates and compares the efficiency of the proposed algorithms in optimizing dynamic offloading decisions, f

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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, et al. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel a

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Xi, Zichen. "Analysis of Adaptive Equalization Algorithms." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 295–305. http://dx.doi.org/10.54097/hset.v70i.12477.

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Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed

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Daumont, Steredenn, and Daniel Le Guennec. "An Analytical Multimodulus Algorithm for Blind Demodulation in a Time-Varying MIMO Channel Context." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/307927.

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This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AM

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LAWLOR, DAVID, YANG WANG, and ANDREW CHRISTLIEB. "ADAPTIVE SUB-LINEAR TIME FOURIER ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 01 (2013): 1350003. http://dx.doi.org/10.1142/s1793536913500039.

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We present a new deterministic algorithm for the sparse Fourier transform problem, in which we seek to identify k ≪ N significant Fourier coefficients from a signal of bandwidth N. Previous deterministic algorithms exhibit quadratic runtime scaling, while our algorithm scales linearly with k in the average case. Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function. This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unalias

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Nariman, Goran Saman, and Hamsa D. Majeed. "Adaptive Filter based on Absolute Average Error Adaptive Algorithm for Modeling System." UHD Journal of Science and Technology 6, no. 1 (2022): 60–69. http://dx.doi.org/10.21928/uhdjst.v6n1y2022.pp60-69.

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Adaptive identification of the bandpass finite impulse response (FIR) filtering system is proposed through this paper using variable step-size least mean square (VSS-LMS) algorithm called absolute average error-based adjusted step-size LMS as an adapted algorithm. This algorithm used to design an adaptive FIR filter by calculating the absolute averaged value for the recently assessed error with the previous one. Then, the step size has been attuned accordingly with consideration of the slick transition of the step size from bigger to smaller to score an achievement through high convergence rat

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Zou, Tingting, and Changyu Wang. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization." Mathematics 10, no. 7 (2022): 1145. http://dx.doi.org/10.3390/math10071145.

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The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the AR

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Korkmaz Tan, Rabia, and Şebnem Bora. "Adaptive parameter tuning for agent-based modeling and simulation." SIMULATION 95, no. 9 (2019): 771–96. http://dx.doi.org/10.1177/0037549719846366.

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The purpose of this study was to solve the parameter-tuning problem of complex systems modeled in an agent-based modeling and simulation environment. As a good set of parameters is necessary to demonstrate the target behavior in a realistic way, modeling a complex system constitutes an optimization problem that must be solved for systems with large parameter spaces. This study presents a three-step hybrid parameter-tuning approach for agent-based models and simulations. In the first step, the problem is defined; in the second step, a parameter-tuning process is performed using the following me

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Chen, Yi Rui, and Yi Zhuang. "An Adaptive Decision Concurrency Control Algorithm." Advanced Materials Research 1046 (October 2014): 512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.512.

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For the lack of adaptability about the existing concurrency control algorithms, adaptive decision concurrency control algorithm is proposed. ADCC algorithm divides concurrency control process into two phases in: execution authorizing phase and strategy selecting phase. In execution authorizing phase, algorithm compares statistics and effectiveness of transactions to determine the execution order of conflict transactions. In strategy selecting phase, according to transactions’ read/write status and current conflict rate, algorithm selects optimistic/pessimistic conflict resolution strategy adap

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Trabia, Mohamed B., and Xiao Bin Lu. "A Fuzzy Adaptive Simplex Search Optimization Algorithm." Journal of Mechanical Design 123, no. 2 (1999): 216–25. http://dx.doi.org/10.1115/1.1347991.

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Most optimization algorithms use empirically-chosen fixed parameters as a part of their search strategy. This paper proposes to replace these fixed parameters by adaptive ones to make the search more responsive to changes in the problem by incorporating fuzzy logic in optimization algorithms. The proposed ideas are used to develop a new adaptive form of the simplex search algorithm whose objective is to minimize a function of n variables. The new algorithm is labeled Fuzzy Simplex. The search starts by generating a simplex with n+1 vertices. The algorithm then repeatedly replaces the point wit

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楊恙, 楊恙, Xukun Zuo Yang Yang, Maosheng Fu Xukun Zuo, Shuhao Yu Maosheng Fu, and Chaochuan Jia Shuhao Yu. "Adaptive Cuckoo Search Algorithm Based on Dynamic Adjustment Mechanism." 電腦學刊 32, no. 5 (2021): 171–83. http://dx.doi.org/10.53106/199115992021103205014.

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Cuckoo Search (CS) algorithm, a simple and effective global optimization algorithm, has been widely used to deal with practical optimization problems. So as to improvethe standard cuckoo search algorithm, such as slow convergence and easy convergence to local optimal value, an Adaptive Cuckoo Search algorithm on the basis of Dynamic Adjustment Mechanism (ACSDAM) has been proposed. Based on exponential function and logarithmic function, the dynamic adjustment is made for updating step size and discovering probability. During the optimization process, updating step size and discovering probabili

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Peng, Shuo, A. J. Ouyang, and Jeff Jun Zhang. "An Adaptive Invasive Weed Optimization Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1559004. http://dx.doi.org/10.1142/s0218001415590041.

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With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorit

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Kobayashi, Masaki, and Yasunori Nagasaka. "Equivalency of SSCF Adaptive Algorithm to Noise Free LMS Adaptive Algorithm." IEEJ Transactions on Electronics, Information and Systems 133, no. 6 (2013): 1173–77. http://dx.doi.org/10.1541/ieejeiss.133.1173.

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Wenwu Liu, Wenwu Liu, Lanlan Kang Wenwu Liu, Qian Du Lanlan Kang, Wenliang Cao Qian Du, and Xiuniao Zhao Wenliang Cao. "Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-Mode Mutation." 電腦學刊 35, no. 6 (2024): 029–43. https://doi.org/10.53106/199115992024123506003.

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<p>In the study of preference-based multi-objective optimization algorithms, the performance significantly depends on the preference information provided by the decision-maker. Over-reliance on this preference information can lead the algorithms to become trapped in locally optimal solutions, potentially overlooking high-quality solutions in other regions. Therefore, this paper proposes a Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-mode Mutation (CPMOP-DM). Firstly, this algorithm utilizes the cubic chaos strategy to initialize population. This strate

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Uddin, Zahoor, Ayaz Ahmad, Muhammad Iqbal, and Zeeshan Kaleem. "Adaptive Step Size Gradient Ascent ICA Algorithm for Wireless MIMO Systems." Mobile Information Systems 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7038531.

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Independent component analysis (ICA) is a technique of blind source separation (BSS) used for separation of the mixed received signals. ICA algorithms are classified into adaptive and batch algorithms. Adaptive algorithms perform well in time-varying scenario with high-computational complexity, while batch algorithms have better separation performance in quasistatic channels with low-computational complexity. Amongst batch algorithms, the gradient-based ICA algorithms perform well, but step size selection is critical in these algorithms. In this paper, an adaptive step size gradient ascent ICA

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Volarić, Ivan, and Victor Sucic. "Adaptive thresholding for sparse image reconstruction." Telfor Journal 15, no. 1 (2023): 8–13. http://dx.doi.org/10.5937/telfor2301008v.

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The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method of decreasing the threshold value as the solution converges toward the optimal one. To address this problem we propose a data-driven adaptive threshold selection method based on the fast intersection of confidence intervals (FICI) method, with w

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Wu, Xidong, Feihu Huang, Zhengmian Hu, and Heng Huang. "Faster Adaptive Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10379–87. http://dx.doi.org/10.1609/aaai.v37i9.26235.

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Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex distributed problem, the federated learning in practice still faces numerous challenges, such as the large training iterations to converge since the sizes of models and datasets keep increasing, and the lack of adaptivity by SGD-based model updates. Meanwhile, the study of adaptive methods in federated learning is scarce and existing works either lack a complete theoretical convergence guarantee or have slow sample com

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Ouyang, Chengtian, Yaxian Qiu, and Donglin Zhu. "Adaptive Spiral Flying Sparrow Search Algorithm." Scientific Programming 2021 (August 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/6505253.

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The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the populatio

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MAYYAS, K. "ANALYSIS OF THE TRANSFORM DOMAIN LMS ALGORITHM WITH INSUFFICIENT LENGTH ADAPTIVE FILTER." Journal of Circuits, Systems and Computers 14, no. 03 (2005): 469–81. http://dx.doi.org/10.1142/s0218126605002441.

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Though, in most practical applications, the length of the adaptive filter is less than that of the unknown system impulse response, analysis of adaptive filtering algorithms almost always assumed a sufficient length adaptive filter whose length is equal to that of unknown system. Theoretical results on the sufficient length adaptive algorithm do not necessarily apply to the realistic insufficient length case and, therefore, it becomes extremely desirable for practical purposes that we quantify the statistical behavior of the insufficient length adaptive algorithm. In this paper, we analyze the

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Chen, Bo, Yilin Zhou, Zhaoyi Li, Jingjing Jia, and Yirui Zhang. "Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm." Sensors 23, no. 9 (2023): 4371. http://dx.doi.org/10.3390/s23094371.

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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic pa

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Chiheb, Amira, and Hassina Khelladi. "Performance Comparison of LMS and RLS Algorithms for Ambient Noise Attenuation." International Journal of Electrical and Computer Engineering Research 4, no. 1 (2024): 14–19. http://dx.doi.org/10.53375/ijecer.2024.383.

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The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one,

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Li, Meng He, Chuan Lin, Jing Bei Tian, and Sheng Hui Pan. "An Algorithms for Super-Resolution Reconstruction of Video Based on Spatio-Temporal Adaptive." Advanced Materials Research 532-533 (June 2012): 1680–84. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1680.

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For the weakness of conventional POCS algorithms, a novel spatio-temporal adaptive super-resolution reconstruction algorithm of video is proposed in this paper. The spatio-temporal adaptive mechanism, which is based on POCS super-resolution reconstruction algorithm, can effectively prevent reconstructed image from the influence of inaccuracy of motion information and avoid the impact of noise amplification, which exist in using conventional POCS algorithms to reconstruct image sequences in dramatic motion. Experimental results show that the spatio-temporal adaptive algorithm not only effective

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Zhang, Yuhao. "Adaptive block level bilateral filtering algorithm." Applied and Computational Engineering 17, no. 1 (2023): 77–85. http://dx.doi.org/10.54254/2755-2721/17/20230917.

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During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filte

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Liu, Qiong, and Tian Yang Li. "Improved Immune Clonal Selection Algorithm and its Application in Power Network Planning." Advanced Materials Research 614-615 (December 2012): 1635–40. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1635.

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Power network planning is a NP hard problem difficult to be solved. It can be contributed to similar TSP problem. Aiming at the slow convergence speed of the traditional immune clonal selection algorithm (ICA), adaptive immune clonal selection algorithm without memory（AICA）and adaptive immune clonal selection algorithm with memory(AICAM) are proposed respectively based on the combination of adaptive algorithm of clonal probability, immune probability , and group disaster algorithm. The two proposed algorithms have been applied to Power network planning problem. The adaptive algorithm has stron

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Yamini Swathi, L., K. S S Soujanya, N. P Lavanya Kumari, and M. Purnachandra Rao. "Study of LMS Algorithm Using Adaptive Filtering Technique." International Journal of Scientific Engineering and Research 3, no. 10 (2015): 66–70. https://doi.org/10.70729/ijser15459.

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Jeyanthi, K. Meena alias, and A. P. Kabilan. "A Simple Adaptive Beamforming Algorithm with interference Suppression." International Journal of Engineering and Technology 1, no. 1 (2009): 67–70. http://dx.doi.org/10.7763/ijet.2009.v1.12.

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Voskoboinikov, Yuri E. "А locally adaptive wavelet filtering algorithm for images". Analysis and data processing systems, № 1 (29 березня 2023): 25–36. http://dx.doi.org/10.17212/2782-2001-2023-1-25-36.

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The algorithms based on the decomposition of a noisy image in an orthogonal basis of wavelet functions have been widely used to filter images (especially contrasting ones) over the past four decades. In this case, most wavelet filtering algorithms are of a threshold nature, namely: the decomposition coefficient smaller in an absolute value of a certain threshold value is reset to zero; otherwise the coefficient undergoes some (most often nonlinear) transformation. A certain (and very significant) drawback of threshold algorithms is that all coefficients of a certain decomposition level are pro

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Wu, Hong Bing, Pei Huang Lou, and Dun Bing Tang. "Adaptive Dynamic Clone Selection Strategy for Optimization." Key Engineering Materials 567 (July 2013): 133–38. http://dx.doi.org/10.4028/www.scientific.net/kem.567.133.

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Based on the Antibody Clonal Selection Theory of immunology, an adaptive dynamic clone select algorithm is put forward. The new algorithm is intended to integrate the local searching with the global and the probability evolution searching with the stochastic searching. Compared with other algorithms, the new algorithm prevents prematurely more effectively and has high convergence speed. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.

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Wang, Zhenwu, Chao Qin, Benting Wan, William Wei Song, and Guoqiang Yang. "An Adaptive Fuzzy Chicken Swarm Optimization Algorithm." Mathematical Problems in Engineering 2021 (March 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/8896794.

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The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of ex

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SMYTH, W. F., and SHU WANG. "AN ADAPTIVE HYBRID PATTERN-MATCHING ALGORITHM ON INDETERMINATE STRINGS." International Journal of Foundations of Computer Science 20, no. 06 (2009): 985–1004. http://dx.doi.org/10.1142/s0129054109007005.

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We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using t

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Raheem, Syed, and Dr Subhashish Bose. "Subband Adaptive Filter in Signal Processing Application." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 4096–109. http://dx.doi.org/10.47059/revistageintec.v11i4.2434.

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Owing to the powerful digital signal processors and the improvement of advanced edge adaptive algorithms there are an extraordinary number of various applications in which adaptive filters are utilized. Subband adaptive filtering algorithms can build the assembly pace of framework ID undertakings when the info signal is hued. The adaptive filter can filter the dubious noise signal, track the difference in the signal, and consistently change the boundaries to accomplish the ideal filtering impact. Another standardized subband adaptive filtering algorithm has been proposed, whose primary benefit

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Mangaonkar, Abhinandan P., Karuna C. Gull, Sushiladevi Vantamuri, Arpita Patil, and Jaya M. Pattanshetti. "Adaptive Energy-Optimized Consolidation Algorithm." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 150–58. http://dx.doi.org/10.17762/ijritcc.v11i5s.6639.

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We have been hearing about cloud computing for quite a long time now. This type of computing is booming and emerging as a popular computing paradigm for its scalability and flexibility in nature. Cloud computing provides the provision of service on-demand, on-demand resources supply and services to end-users. However, energy consumption and energy wastage are becoming a major concern for cloud providers due to its direct impression on costs required for operations and carbon emissions. To tackle this issue, Adaptive Energy-Optimized Consolidation Algorithm has been proposed to efficiently mana

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Chai, Ruishuai. "Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm." Complexity 2021 (March 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5564690.

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmen

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Bagirov, Adil, Sona Taheri та Burak Ordin. "AN ADAPTIVE 𝑘-MEDIANS CLUSTERING ALGORITHM". Problems of Information Technology 13, № 2 (2022): 3–15. http://dx.doi.org/10.25045/jpit.v13.i2.01.

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A new version of the k-medians algorithm, the adaptive k-medians algorithm, is introduced to solve clustering problems with the similarity measure defined using the L1-norm. The proposed algorithm first calculates the center of the whole data set as its median. To solve the k-clustering problem (k-1), we formulate the auxiliary clustering problem to generate a set of starting points for the k-th cluster center. Then, the k-medians algorithm is applied starting from the previous (k-1) cluster centers and each point from the set of starting points to solve the k-clustering problem. A solution wi

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Chang, Chun Yuan. "An Adaptive Algorithm for Forest Fire Spread Based on Genetic Algorithm." Advanced Materials Research 694-697 (May 2013): 2842–45. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2842.

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The spread of forest fire is a complex adaptive system. The spread could be seen as the result of fire agents continuous learning, adaptation and co-ordination. This paper founded an Agent-based model for forest fire spread, modeled the generating of fire spread rules based on Genetic Algorithms. Created the spread rules with effect of wind and topography independently for forest fire, designed the fitness function, and took the genetic operation on the rules, which created new rules. Implemented the adaptive algorithm on Repast S, and used it in the Agent-based model of forest fire spread. Th

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Asadi, Davood, and Karim Ahmadi. "Nonlinear robust adaptive control of an airplane with structural damage." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 14 (2020): 2076–88. http://dx.doi.org/10.1177/0954410020926618.

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This article investigates the design of a novel nonlinear robust adaptive control architecture to stabilize and control an airplane in the presence of left-wing damage. Damage effect is modeled by considering the sudden mass and inertia changes, center of gravity, and aerodynamic variations. The novel nonlinear control algorithm applies a state predictor as well as the error between the real damaged dynamics and a virtual model based on the nominal aircraft dynamics in the control loop of the adaptive strategy. The projection operator is used for the purpose of robustness of the adaptive contr

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Haario, Heikki, Eero Saksman, and Johanna Tamminen. "An Adaptive Metropolis Algorithm." Bernoulli 7, no. 2 (2001): 223. http://dx.doi.org/10.2307/3318737.

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Linovich, A. Yu, V. S. Litvinova, and M. D. Korolev. "COMB ADAPTIVE FILTERING ALGORITHM." Vestnik of Ryazan State Radio Engineering University 77 (2021): 3–16. http://dx.doi.org/10.21667/1995-4565-2021-77-3-16.

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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 develop

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FUJII, Kensaku, and Mitsuji MUNEYASU. "Reconsideration of Adaptive Algorithm." IEICE ESS Fundamentals Review 8, no. 4 (2015): 292–313. http://dx.doi.org/10.1587/essfr.8.292.

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Maheshwari, J., and N. V. George. "Polynomial sparse adaptive algorithm." Electronics Letters 52, no. 25 (2016): 2063–65. http://dx.doi.org/10.1049/el.2016.3747.

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SASTRI, T. "An Adaptive Estimation Algorithm." IIE Transactions 20, no. 2 (1988): 176–85. http://dx.doi.org/10.1080/07408178808966167.

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Kresoja, Milena, Zorana Lužanin, and Irena Stojkovska. "Adaptive stochastic approximation algorithm." Numerical Algorithms 76, no. 4 (2017): 917–37. http://dx.doi.org/10.1007/s11075-017-0290-4.

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Kelly, E. J. "An Adaptive Detection Algorithm." IEEE Transactions on Aerospace and Electronic Systems AES-22, no. 2 (1986): 115–27. http://dx.doi.org/10.1109/taes.1986.310745.

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