• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.559-570
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• 2008

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

• 한국통신학회논문지
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• 제30권3C호
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• pp.141-146
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• 2005

The authors investigate the convergence speed problem of nonlinear adaptive equalization. Convergence constraints and time constant of radial basis function network using stochastic gradient (RBF-SG) algorithm is analyzed and a method of making time constant independent of hidden-node output power by using sample-by-sample node output power estimation is derived. The method for estimating the node power is to use a single-pole low-pass filter. It is shown by simulation that the proposed algorithm gives faster convergence and lower minimum MSE than the RBF-SG algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.87-92
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• 2002

배치(Placement)는 VLSI 회로의 physical design에서 중요한 단계로서 회로의 성능을 최대로 하기 위하여 회로 모듈의 집합을 배치시키는 문제이며, 배치 문제에서 최적의 해를 얻기 위해 클러스터 성장(cluster growth), 시뮬레이티드 어닐링(simulated annealing; SA), ILP(integer linear programming)등의 방식이 이용된다. 본 논문에서는 배치 문제에 대하여 확률 진화 알고리즘(stochastic evolution algorithm; StocE)을 이용한 해 공간 탐색(solution space search) 방식을 제안하였으며, 제안한 방식을 시뮬레이티드 어닐링 방식과 비교, 분석하였다.

• Journal of Ubiquitous Convergence Technology
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• 제2권2호
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• pp.105-110
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• 2008

The definition of the program control is introduced on the theory of the basis of the first integrals SDE system. That definition allows constructing the program control gives opportunity to stochastic system to remain on the given dynamic variety. The program control is considered in terms of dynamically invariant for stochastic process.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제18권1호
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• pp.43-50
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• 2014

In this paper we discussed Euler-Maruyama method for stochastic differential equations with jump diffusion. We give a convergence result for Euler-Maruyama where the coefficients of the stochastic differential equation are locally Lipschitz and the pth moments of the exact and numerical solution are bounded for some p > 2.

• Korean Journal of Mathematics
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• 제22권1호
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• pp.71-84
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• 2014

By using the multidimensional normal approximation of functionals of Gaussian fields, we prove that functionals of Gaussian fields, as functions of t, converge weakly to a standard Brownian motion. As an application, we consider the convergence of the Stratonovich-type Riemann sums, as a function of t, of fractional Brownian motion with Hurst parameter H = 1/4.

• 전기학회논문지
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• 제57권10호
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.623-625
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• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• 천문학논총
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• 제22권4호
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• pp.103-111
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• 2007

On the formulation frameworks of linearly perturbed spacetime and weak gravitational lensing(WGL) we studied the statistical properties of a bundle of light rays propagating through stochastic gravitational wave background(SGWB). For this we considered the SGWB as tensor perturbations of linearly perturbed Friedmann spacetime. Using the solution of null geodesic deviation equation(NGDE) we related the convergence, shear and rotation deformation spectra of WGL with the strain spectra of SGWB. Adopting the astrophysical and cosmological SGWB strain spectra which were already known we investigated the approximated spectral forms of convergence, shear and rotation of WGL.