• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.1-8
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• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3086-3097
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• 1996

Generally, the performance of the computer network may differ according to various parameters like routing, bandwidth and timers. Since the network system requirements may widely very according to specific application, computer network must be tailored at tailored at the design starge by selection of appropriate protocols and assignment of degfault parameter. However, since the condition under which a network actually operates may change from that considered at the design stage, control and managment action are required to adjust the network parameter so that the perforance of network is satisfied.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.901-904
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• 1998

In this paper, we describes PID-neural network controller for the rotary inverted pendulum. PID control is applied to many fields but has some problems in nonlinear system due to a variation of parameter. So, we should desing the controller which is adjusted PI parameters by the neural network which is learned by backpropagation algorithm. And we show that on-line control is possible through the PID-neural network controller. The angle of the pendulum is controlled and then the position of the rotating arm is also controlled to maintain with in the set point. Measurement of the pendulum angle is obtained using a potentionmeter. The objective of the experiment is to design a PID-neural network control system that positions the arm as well as maintains the ivnerted pendulum vertical. Finally, we describe the actual experiment system and confirm the experimental results.

• ETRI Journal
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• v.41 no.4
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• pp.452-464
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• 2019

The use of wireless body area networks (WBANs) in healthcare applications has made it convenient to monitor both health personnel and patient status continuously in real time through wearable wireless sensor nodes. However, the heterogeneous and complex network structure of WBANs has some disadvantages in terms of control and management. The software-defined network (SDN) approach is a promising technology that defines a new design and management approach for network communications. In order to create more flexible and dynamic network structures in WBANs, this study uses the SDN approach. For this, a WBAN architecture based on the SDN approach with a new energy-aware routing algorithm for healthcare architecture is proposed. To develop a more flexible architecture, a controller that manages all HUBs is designed. The proposed architecture is modeled using the Riverbed Modeler software for performance analysis. The simulation results show that the SDN-based structure meets the service quality requirements and shows superior performance in terms of energy consumption, throughput, successful transmission rate, and delay parameters according to the traditional routing approach.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.6
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• pp.677-682
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• 2014

In the present study, a decision tree and artificial neural network were used to determine critical design parameters for lithium ion batteries and compare their performances. First, a design method that used a decision tree-artificial neural network model was used to determine the major design factors among early pole plate design factors that showed nonlinearity. Then, the artificial neural network was used to implement a weighted value analysis of the importance of the design factors and their effect on the current density. The second method involved the use of an artificial neural network model to construct artificial networks without separate determinations of the major early design factors to analyze the connections and weighted values related to the current density.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.328-332
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• 1996

B-ISDN is expected to be a main vehicle of future telecommunications. There has been a series fo studies on the demand and the market prospect of B-ISDN services. It is true, however, that they lacked some economic reality since the price of services has been overlooked which is a critical economic factor. In this study, we analyze some aspects of the tariff system of B-ISDN services. First, we explore and summarize the billing parameters of B-ISDN services from diverse characteristics of services and ATM network. These parameters are essential if the services are to be charged based on usage. Secondly, we discuss what factors be considered in the design fo B-ISDN services tariff systems from various points of view shch as traffic charactristics, information types and connection types, etc. The results of this study will offer fundamental insights in the design of B-ISDN service pricing scheme and provide reference for efficient services providing.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.68-70
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• 2004

In this paper we develop an adaptive interface between video compression and transission protocols to handle QoS fluctuations that are common to mobile communication systems. We consider various generic design alternatives for QoS adaptation and identify 'QoS negotiation' as the most promissing. This method gives the best possibilities to obtain system-wide efficiency. To handle the indued system complexity we apply a design philosophy (called ARC) that separates implementation dependencies by introducing QoS interfaces between system modules. In the ARC phlosophy the implementation details are hidden in the subsystems. To assure efficient adaptation, the QoS must be negotiated between modules. We select the QoS parameters that are both necessary and sufficient for efficient negotiation between the video encoder and protocol modules. We describe the relation between the QoS parameters at the interface and the internal parameters of common video coding methods and protocol elements. Furthermore, we describe a negotiation procedure that allows a system-wide optimum to emerge.