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A kernelization algorithm for d-Hitting Set
Published 2010“…For a given parameterized problem, π, a kernelization algorithm is a polynomial-time pre-processing procedure that transforms an arbitrary instance of π into an equivalent one whose size depends only on the input parameter(s). …”
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2
A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…In this paper we present a parallel algorithm to solve the above problem for arbitrary graphs using a Hopfield Neural Network model of computation. …”
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A stochastic iterative learning control algorithm with application to an induction motor
Published 2004“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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4
Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise
Published 2005“…This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. …”
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Fuzzy Logic Adaptive Crow Search Algorithm for MPPT of a Partially Shaded Photovoltaic System
Published 2024“…<p dir="ltr">The arbitrary selection of the Crow Search Algorithm (CSA) parameters, the Awareness Probability (AP) and the Flight Length (fl) results in poor convergence performance and efficiency even if the CSA performs well when solving global optimization problems. …”
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An improved kernelization algorithm for r-Set Packing
Published 2010“…Such parameterized reductions are known as kernelization algorithms, and a reduced instance is called a problem kernel. …”
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7
A method for the minimum coloring problem using genetic algorithms
Published 2006“…This paper presents a method to solve the graph coloring problem for arbitrary graphs using genetic algorithms. The graph coloring problem, an NP-hard problem, has important applications in many areas including time tabling and scheduling, frequency assignment, and reg ister allocation. …”
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Optimal selection of the forgetting matrix into an iterative learning control algorithm
Published 2005“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the optimal forgetting matrix and the learning gain matrix of a P-type iterative learning control (ILC) for linear discrete-time varying systems with arbitrary relative degree. …”
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A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†
Published 2010“…This paper presents a hardware implementation to solve the graph colouring problem (chromatic number χ(G)) for arbitrary graphs using the Hopfield neural network (HNN) model of computation. …”
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10
Complexities of special matrix multiplication problems
Published 1988“…This paper develops optimal algorithms to multiply an n × n symmetric tridiagonal matrix by: (i) an arbitrary n × m matrix using 2nm − m multiplications; (ii) a symmetric tridiagonal matrix using 6n − 7 multiplications; and (iii) a tridiagonal matrix using 7n −8 multiplications. …”
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A MIMO Sampling-Rate-Dependent Controller
Published 2014“…The SRD controller aims at achieving uniform output tracking in the sense of attaining arbitrary small steady-state errors as well as arbitrary small settling time. …”
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Fast, effective vertex cover kernelization
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Crown Structures for Vertex Cover Kernelization
Published 2007“…Crown structures in a graph are defined and shown to be useful in kernelization algorithms for the classic vertex cover problem. Two vertex cover kernelization methods are discussed. …”
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15
The architecture of a highly reconfigurable RISC dataflow array processor
Published 2020“…The array can be programmed to execute arbitrary algorithms in both static and dynamic manner. …”
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Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems
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17
Metaheuristic Optimization‐Based Sliding Mode Control With Modified Perturb and Observe for Controlling MPPT of a PV Interfaced Grid Connected System
Published 2025“…Also, the proposed hybrid (MGO‐MPb&O) is compared with two other hybrid control topologies that are (PSO‐MPb&O) and (cuckoo search algorithm [CSA]‐MPb&O) in terms of maximum power extracted, efficiency, and convergence time of the objective function. …”
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Learn while Tracking
Published 2007“…Arbitrary small settling time along with arbitrary small steady-state output error are studied. …”
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A quadratic kernel for 3-set packing
Published 2017“…Such parameterized reductions are known as kernelization algorithms, and each reduced instance is called a problem kernel. …”
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20
Efficient Seismic Volume Compression using the Lifting Scheme
Published 2000“…In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. …”
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