• 전자공학회논문지S
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• 제35S권4호
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• 산업경영시스템학회지
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• 제38권2호
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• pp.120-128
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• 2015

Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.

• 전기학회논문지
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• 제67권9호
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• pp.1174-1180
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• 2018

In December 2017, the government announced plans to increase the current proportion of renewable energy from 7% to 20% by 2030 through a plan called the Renewable Energy 3020 Implementation Plan. Therefore, the demand for installation of photovoltaic(PV), wind turbine(WT) and battery energy storage system(BESS) is expected to increase. In particular, the system combined with energy storage system(ESS) is expected to take up a large portion since PV and WT can receive high renewable energy certificates(REC) weights when combined with ESS. In this study, we calculate the optimal capacity of the power conditioning system(PCS) and the BESS by comparing the economical efficiency and maximize the efficiency of the PV-BESS system in which the PV and the BESS are connected. By analyzing the system marginal price(SMP) and REC, it maximize profits through application of REC weight 5.0 and optimal charge-discharge scheduling according to the SMP changes.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 제11회 음성통신 및 신호처리 워크샵 논문집 (SCAS 11권 1호)
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• pp.223-228
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• 1994

We propose a novel method to incorporate temporal correlations into a speech recognition system based on the conventional hidden Markov model. With the proposed method using the extended logarithmic pool, we approximate a joint conditional PD by separate conditional PD's associated with respective components of conditions. We provide a constrained optimization algorithm with which we can find the optimal value for the pooling weights. The results in the experiments of speaker-independent continuous speech recognition with frame correlations show error reduction by 13.7% with the proposed methods as compared to that without frame correlations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1655-1658
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• 1991

This paper considers the design and analysis of one-step ahead optimal and adaptive controllers, under the restriction that a known constraint on the input amplitude is imposed. It is assumed that the discrete-time single-input, single-output system to be controlled is linear, except for inequality constraints on the input. The objective function to be minimized is an one-step quadratic function, where polynomial weights on the input and output are included. Both the known parameter and unknown parameter (indirect adaptive controller) cases are examined.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.884-889
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• 1989

In this paper, a new procedure for selecting weighting matrices in linear discrete time quadratic optimal control problem (LQ-problem) is proposed. In LQ-problems, the quadratic weighting matrices are usually decided on trial and error in order to get a good response. But using the proposed method, the quadratic weights are decided in such a way that all poles of the closed loop system are located in a desired region for good responses as well as for stability and values of the quadratic cost function are kept less then a specified value.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.214-217
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• 1999

In this paper, genetic algorithm (GA) is implemented to search for the optimal structures (i.e. the kind of neural networks, the number of inputs and hidden neurons) of neural networks which are used approximating a given nonlinear function. Two kinds of neural networks, i.e. the multilayer feedforward [1] and time delay neural networks (TDNN) [2] are involved in this paper. The synapse weights of each neural network in each generation are obtained by associated training algorithms. The simulation results of nonlinear function approximation are given out and some improvements in the future are outlined.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.462-462
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• 2000

In this paper, genetic algorithm (GA) is the technique to search for the optimal structures (i,e., the kind of neural network, the number of hidden neuron, ..) of the neural networks which are used approximating a given nonlinear function, In this paper, we used multi layer feed-forward neural network. The decision method of synapse weights of each neuron in each generation used back-propagation method. In this study, we simulated nonlinear function approximation in the temperature control system.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.991-997
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• 2006

Recently, Liu (2005) proposed a special type of weighting function under a given preference index level with the minimal variability similar to the minimal variability OWA operator weights problem proposed by Fuller and Majlender (2003). He solved this problem using a result of classical optimal control theory. In this note, we give a direct elementary proof of this problem without using any known results.

• 대한산업공학회지
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• 제22권4호
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• pp.753-770
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• 1996

In order to reduce the number of rehandles during the loading operation of export containers in port container terminals, the storage location of each arriving container should be determined considering of its weight. We formulate the problem by a dynamic programming model to get the optimal storage location. And a heuristic rule is suggested in order to overcome computational difficulties of the optimization model. The performance of the rule is evaluated by comparing it with the result of DP model.