• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.838-841
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• 2005

DNA Chip is able to show DNA-Data that includes diseases of sample to User by using complementary characters of DNA. So this paper studied Neural Network algorithm for Image data processing of DNA-chip. DNA chip outputs image data of colors and intensities of lights when some sample DNA is putted on DNA-chip, and we can classify pattern of these image data on user pc environment through artificial neural network and some of image processing algorithms. Ultimate aim is developing of pattern classifying algorithm, simulating this algorithm and so getting information of one's diseases through applying this algorithm. Namely, this paper study artificial neural network algorithm for classifying pattern of image data that is obtained from DNA-chip. And, by using histogram, gradient edge, ANN and learning algorithm, we can analyze and classifying pattern of this DNA-chip image data. so we are able to monitor, and simulating this algorithm.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.488-492
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• 1993

This paper proposes a new approach to the optimization method of a blending process with neural network. The method is based on the error backpropagation learning algorithm for neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a system solver. A fuzzy membership function is used in parallel with the neural network to minimize the difference between measurement value and input value of neural network. As a result, we can guarantee the reliability and stability of blending process by the help of neural network and fuzzy membership function.

• 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.

• 한국산업융합학회 논문집
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• 제17권2호
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• pp.69-76
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• 2014

This paper proposes a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.953-958
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• 1993

We develop a neural network control algorithm for the ACS (Advanced Control System). The ACS is an extended version of the DCS (Distributed Control System) to which functions of fault detection and diagnosis and advanced control algorithms are added such as neural networks, fuzzy logics, and so on. In spite of its usefulness proven by computer simulations, the neural network control algorithm, as far as we know, has no tool which makes it applicable to process control. It is necessary that the neural network controller should be turned into the function code for its application to the ACS. So we develop a general method to implement the neural network control systems for the ACS. By simulations using the simulator for the boiler of 'Seoul fire power plant unit 4', the methodology proposed in this paper is validated to have the applicability to process control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.19-19
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• 2000

This paper introduces a new scheme for neural network classifier which can describe the shape of patterns in clustered group by using a self-organizing teeming algorithm. The prototype based neural network classifier can not describe the shape of group and it has low classification performance when the data groups are complex. To improve above-mentioned problem, new neural scheme is introduced. This proposed neural network algorithm can be regarded as the extension of self-organizing feature map which can describe The experimental results shows that the proposed algorithm can describe the shape of pattern successfully.

• 전자공학회논문지B
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• 제33B권12호
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• pp.95-105
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• 1996

This paper presents structure optimization of a feedforward neural netowrk controller using the genetic algorithm. It is important to design the neural network with minimum structure for fast response and learning. To minimize the structure of the feedforward neural network, a genralization of multilayer neural netowrks, the genetic algorithm uses binary coding for the structure and floating-point coding for weights. Local search with an on-line learnign algorithm enhances the search performance and reduce the time for global search of the genetic algorithm. The relative fitness defined as the multiplication of the error and node functions prevents from premature convergence. The feedforward neural controller of smaller size outperformed conventional multilayer perceptron network controller.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.324-334
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• 2016

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

• 전자공학회논문지CI
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• 제44권2호
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• pp.11-18
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• 2007

신경회로망의 학습에 널리 사용되고 있는 오차역전파 알고리즘은 최급하강법을 기초로 하고 있기 때문에 초기값에 따라서는 극소값에 떨어지거나, 신경회로망을 학습시킬 때 중간층 유닛수를 얼마로 설정하는 등의 문제점이 있다. 따라서 이러한 문제점을 해결하기 위하여, 본 논문에서는 3비트 패리티 판별을 위하여 신경회로망의 학습에 교차법, 돌연변이법에 새로운 기법을 도입한 개량형 유전적 알고리즘을 제안한다. 본 논문에서는 세대차이, 중간층 유닛수의 차이, 집단의 개체수의 차이에 대하여 실험을 실시하여, 본 방식이 학습 속도의 면에서 유효하다는 것을 나타낸다.

• 한국지능시스템학회논문지
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• 제10권4호
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• pp.338-342
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• 2000

In order to approximate a nonlinear function, wacelet neural networks combining wacelet theory and neural networks have been proposed as an alternative to conventional multi-layered neural networks. wacelet neural networks provide better approximating performance than conventional neural networks. In this paper, an effective method to construct an optimal wavelet neural network is proposed using genetic alogorithm. Genetic Algorithm is used to determine dilationa and translations of wavelet basic functions of wavelet neural networks. Then, these determined dilations dilations and translations, wavelet neural networks are funther trained by back propagation learning algorithm. The effectiveness of the final network is verified thrifigh the approximation result of a nonlinear function and comparison with conventional neural networks.