• Journal of Ocean Engineering and Technology
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• v.20 no.5 s.72
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• pp.70-76
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• 2006

A Probabilistic neural network (PNN) technique for predicting the stability number for the armor blocks of breakwaters is presented. A PNN is prepared using the experimental data of van der Meer and is then compared with the empirical formula and previous artificial neural network (ANN) model. This comparison shows that a PNN can effectively predict the stability numbers in spite of data complexity, incompleteness, and incoherence, and can be an effective tool for the designers of rubble mound breakwaters to support their decision process and to improve design efficiency.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.133-136
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• 2001

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(AJT) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent hJT control to that of the conventional shift pattern.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.267-270
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• 2001

In this paper, we propose the self-autonomous algorithm for mobile robot system. The proposed mobile robot system which is teamed by learning with the neural networks can trace the target at the same distances. The mobile robot can evaluate the distance between robot and target with ultrasonic sensors. By teaming the setup distance, current distance and command velocity, the robot can do intelligent self-autonomous drive. We use the neural network and back-propagation algorithm as a tool of learning. As a result, we confirm the ability of tracing the target with proposed mobile robot.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1964-1965
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• 2011

본 연구에서는 패턴분류를 위해 기존의 방사형 기저 함수 신경회로망(Radial Basis Funtion Neural Network)과 다항식 신경회로망(Polynomial Neural Network)을 결합한 다중 출력 방사형 기저 함수다항식 신경회로망 (Multi Output Radial Basis Funtion Polynomial Neural Network)의 분류기를 제안한다. 제안된 모델은 PNN을 기본 구조로 하여 1층에 기존의 다항식 노드 대신 다중 출력 형태의 RBFNN을 적용 한다. RBFNN의 은닉층에는 기존의 활성함수가 아닌 fuzzy 클러스터링을 사용하여 입력 데이터의 특성을 고려한 적합도를 사용하였다. PNN은 입력변수의 수와 다항식 차수가 모델의 성능을 결정함으로 최적화가 필요하며 본 논문에서는 Differential Evolution(DE)을 사용하여 모델의 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시켰다. 패턴분류기로써의 제안된 모델을 평가하기 위해 pima 데이터를 이용하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.89-94
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• 1995

A16061, SB41 and SM45C was used for developing tool wear monitoring system in face milling. First of all, Neural networks of which input are 8 $_{th}$ order AR morel parameters, frequency band energies, cutting conditions was used to monitor tool wear for each material. Finally, A unified neural network, which has tensile strengths of each material as an additional input, was constructed to consider the effect three materials on the features of tool wear. It was verified that tensile strength is the one of properties of workpiece materials.s.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.108-114
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• 2008

Polynomial networks have been known to have excellent properties as classifiers and universal approximators to the optimal Bayes classifier. In this paper, the use of polynomial neural networks is proposed for efficient implementation of the polynomial-based classifiers. The polynomial neural network is a trainable device consisting of some rules and three processes. The three processes are assumption, effect, and fuzzy inference. The assumption process is driven by fuzzy c-means and the effect processes deals with a polynomial function. A learning algorithm for the polynomial neural network is developed and its performance is compared with that of previous studies.

• Korean Journal of Human Ecology
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• v.12 no.1
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• pp.63-73
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• 2003

The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.13-24
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• 1999

In this paper, we propose a classifier based on modular networks using an unsupervised learning method. The structure of each module is designed through stochastic analysis of input data and each module classifier data independently. The result of independent classification of each module and a measure of the nearest distance are integrated during the final data classification phase to allow more precise c classification. Computation time is decreased by deleting modules that have been classified to be incorrect during the final classification phase. Using this method. a neural network sharing the best performance was implemented without considering. lots of of variables which can affect the performance of the neural network.