• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.124-129
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• 2002

This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.749-756
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• 2006

This paper presents a complex stochastic wheelbase preview control method of a motorcycle suspension based on hierarchical modeling method. As usual, a vehicle suspension system is controlled as a whole body. In this method, a motorcycle suspension with five Degrees of Freedom(DOF) is dealt with two local independent 2-DOF suspensions according to the hierarchical modeling method. The central dynamic equations that harmonize local relations are deduced. The vertical and pitch accelerations of the suspension center are treated as center control objects, and two local semi-active control forces can be obtained. In example, a real time Linear Quadratic Gaussian(LQG) algorithm is adopted for the front suspension and the combination of the wheelbase preview and LQG control method is designed for the rear suspension. The results of simulation show that the control strategy has less calculating time and is convenient to adopt different control strategies for front and rear suspensions. The method proposed in this paper provides a new way for the vibration control of multi-wheel vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5218-5233
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• 2018

The coexisting wireless body area networks (WBAN) is a very challenging issue because of strong inter-networks interference, which seriously affects energy consumption and spectrum utilization ratio. In this paper, we study a power control strategy with nearest neighbor nodes distribution for coexisting WBAN based on stochastic geometry. Using homogeneous Poisson point processes (PPP) model, the relationship between the transmission power and the networks distribution is analytically derived to reduce interference to other devices. The goal of this paper is to increase the transmission success probability and throughput through power control strategy. In addition, we evaluate the area spectral efficiency simultaneously active WBAN in the same channel. Finally, extensive simulations are conducted to evaluate the power control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.435-439
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• 1998

A control strategy for flexible structure under irregular disturbance by using of$\boxDr$random gain$\boxUl$is developed and implemented. System equation is transformed to stochastic domain by F-P-K approach from physical domain. A controller is designed in the stochastic domain, accordingly system is controlled by$\boxDr$random gain$\boxUl$in time domain. In the paper, a new control technique is successfully employed for flexible system under white noise, and the result is verified by Monte-Carlo simulation and compared with the performance via LQR controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.319-326
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• 2015

Recently in the fields o f stochastic optimal control ( SOC) and reinforcemnet l earning (RL), there have been a great deal of research efforts for the problem of finding data-based sub-optimal control policies. The conventional theory for finding optimal controllers via the value-function-based dynamic programming was established for solving the stochastic optimal control problems with solid theoretical background. However, they can be successfully applied only to extremely simple cases. Hence, the data-based modern approach, which tries to find sub-optimal solutions utilizing relevant data such as the state-transition and reward signals instead of rigorous mathematical analyses, is particularly attractive to practical applications. In this paper, we consider a couple of methods combining the modern SOC strategies and approximate inference together with machine-learning-based data treatment methods. Also, we apply the resultant methods to a variety of application domains including financial engineering, and observe their performance.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.620-625
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• 2008

The $H_{\infty}$ entropy in $H_{\infty}$ control theory is discussed based on investigating information transmission in continuous-time linear stochastic systems. It is proved that the stabilizing feedback does not change the time-average information transmission between system input and output, and the $H_{\infty}$ entropies of open- and closed-loop stable transfer functions are bounded by mutual information rate between input and output in the open-loop system. Furthermore, a new $H_2$ upper bound for $H_{\infty}$ entropy is introduced with a numerical example. Thus the $H_{\infty}$ entropy of a stable transfer function is sandwiched between $H_2$ norms of the original system and a static feedback system.

• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.49-55
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• 2009

In this paper, we investigate an optimal allocation of constant service resources in stochastic system to optimize the expected performance of interest. For this purpose, we use the control variates to estimate the gradients of expected performance with respect to given resource parameters, and apply these estimated gradients in stochastic optimization algorithm to find the optimal allocation of resources. The proposed gradient estimation method is advantageous in that it uses simulation results of a single design point without increasing the number of design points in simulation experiments and does not need to describe the logical relationship among realized performance of interest and perturbations in input parameters. We consider the applications of this research to various models and extension of input parameter space as the future research.

• 전기의세계
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• v.30 no.11
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• pp.728-733
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• 1981

Most of real world systems are highly non-linear. But due to difficulties in analyzing and dealing with it, only the linear system theory is well estabilished. Bilinear system where state and control are linear but not linear jointly is introduced. Here shows that optimal state estimation of stochastic bilinear system requirs infinite dimensional filter, thus onesub-optimal estimator for this system is suggested.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.139-144
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• 1993

In this paper, a robust reduced order adaptive controller is proposed based on Internal Model Control(IMC) structure for stochastic linear stable systems. The concept of gain margin is utilized to make the adaptive IMC controller robust. We prove the stability of the proposed adaptive IMC system for stable plants under the assumption that upper bounds for system orders are known. Simulation results show that the proposed method has good performance and stability robustness.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.33.4-33
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• 2001

This paper considers a stochastic discrete algebraic Riccati equation, which is a generalized version of the well-known standard discrete algebraic Riccati equation, and has additional linear terms. Under controllability, observability and the assumption that the additional terms are not too large, the existence of a positive definite solution is guaranteed. It is shown that it arises in optimal control of a linear discrete-time system with multiplicative White noise and quadratic cost. A numerical example is given.