• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.941-946
• /
• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.29-34
• /
• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Journal of Drive and Control
• /
• v.17 no.1
• /
• pp.13-20
• /
• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Structural Engineering and Mechanics
• /
• v.6 no.8
• /
• pp.955-969
• /
• 1998

Engineering problems are inherently imprecision tolerant. Biologically inspired soft computing methods are emerging as ideal tools for constructing intelligent engineering systems which employ approximate reasoning and exhibit imprecision tolerance. They also offer built-in mechanisms for dealing with uncertainty. The fundamental issues associated with engineering applications of the emerging soft computing methods are discussed, with emphasis on neural networks. A formalism for neural network representation is presented and recent developments on adaptive modeling of neural networks, specifically nested adaptive neural networks for constitutive modeling are discussed.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.468-473
• /
• 1999

Neural networks for adaptive channel equalizers have been resorted to recently in digital communication systems. In this paper, the characteristics and the application areas and etc. for neural networks as adaptive channel equalizers are examined through simple examples.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2005.10a
• /
• pp.17-22
• /
• 2005

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection c! the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented.

• Journal of Navigation and Port Research
• /
• v.29 no.9
• /
• pp.771-776
• /
• 2005

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection of the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances will be presented.

• Journal of Communications and Networks
• /
• v.7 no.2
• /
• pp.126-134
• /
• 2005

Reducing energy consumption in mobile hosts (MHs) is one of the most critical issues in wireles/mobile networks. IP paging protocol at network layer and power saving mechanism (PSM) at link layer are two core technologies to reduce the energy consumption of MHs. First, we investigate the energy efficiency of the current IEEE 802.11 power saving mechanism (PSM) when IP paging protocol is deployed over IEEE 802.11 networks. The result reveal that the current IEEE 802.11 PSM with a fixed wakeup interval (i.e., the static PSM) exhibits a degraded performance when it is integrated with IP paging protocol. Therefore, we propose an adaptive power saving mechanism in IEEE 802.11-based wireless IP networks. Unlike the static PSM, the adaptive PSM adjusts the wake-up interval adaptively depending on the session activity at IP layer. Specifically, the MH estimates the idle periods for incoming sessions based on the exponentially weighted moving average (EWMA) scheme and sets its wake-up interval dynamically by considering the estimated idle period and paging delay bound. For performance evaluation, we have conducted comprehensive simulations and compared the total cost and energy consumption, which are incurred in IP paging protocol in conjunction with various power saving mechanisms: The static PSM, the adaptive PSM, and the optimum PSM. Simulation results show that the adaptive PSM provides a closer performance to the optimum PSM than the static PSM.

• Journal of Information Processing Systems
• /
• v.13 no.6
• /
• pp.1436-1458
• /
• 2017

Quorum-based algorithms are widely used for solving several problems in mobile ad hoc networks (MANET) and wireless sensor networks (WSN). Several quorum-based protocols are proposed for multi-hop ad hoc networks that each one has its pros and cons. Quorum-based protocol (QEC or QPS) is the first study in the asynchronous sleep scheduling protocols. At the time, most of the proposed protocols were non-adaptive ones. But nowadays, adaptive quorum-based protocols have gained increasing attention, because we need protocols which can change their quorum size adaptively with network conditions. In this paper, we first introduce the most popular quorum systems and explain quorum system properties and its performance criteria. Then, we present a comparative and comprehensive survey of the non-adaptive and adaptive quorum-based protocols which are subsequently discussed in depth. We also present the comparison of different quorum systems in terms of the expected quorum overlap size (EQOS) and active ratio. Finally, we summarize the pros and cons of current adaptive and non-adaptive quorum-based protocols.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.236-241
• /
• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.