• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.187-197
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• 2004

The recycling cell formation problem means that disposal products are classified into recycling product families using group technology in their end-of-life phase. Disposal products have the uncertainties of product condition usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a new approach for the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy ART neural networks are applied to describe the condition of disposal product with the membership functions and to make recycling cell formation. The approach leads to cluster materials, components, and subassemblies for reuse or recycling and can evaluate the value at each cell of disposal products. Disposal refrigerators are shown as an example.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.21-29
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• 2006

A fuzzy ARTMAP neural network and a fuzzy ART neural network are proposed to identify $H_2S,\;NH_3$, and their mixtures and to estimate their concentrations, respectively. Features are extracted from a tin oxide gas sensor operated in a thermal modulation plan. After dimensions of the features are reduced by a preprocessing scheme, the features are fed into the proposed fuzzy neural networks. By computer simulations, the proposed method is shown to be fast in learning and stable in concentration estimating compared with other methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.432-437
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• 1997

In this paper, Fuzzy ART which is one of the competitive learing neural networks is applied to machine-part cell formation problem. A large matrix and varios types of machine-part incidence matrices, especially including bottle-neck machines, bottle-neck parts, parts shared by several cells, and machines shared by several cells are used to test the performannce of Fuzzy ART neural network as a cell formation algorithm. The result shows Fuzzy ART neral network can be efficiently applied to machine-part cell formation problem which are large, and/or have much imperfection as exceptions, bottle-neck machines, and bottle-neck parts.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1312-1320
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• 2003

In this paper, we consider a multilayer neural network, with a single hidden layer. Error backpropagation learning method used widely in multilayer neural networks has a possibility of local minima due to the inadequate weights and the insufficient number of hidden nodes. So we propose a fuzzy supervised learning algorithm by using self-generation that self-generates hidden nodes by the compound fuzzy single layer perceptron and modified ART1. From the input layer to hidden layer, a modified ART1 is used to produce nodes. And winner take-all method is adopted to the connection weight adaptation, so that a stored pattern for some pattern gets updated. The proposed method has applied to the student identification card images. In simulation results, the proposed method reduces a possibility of local minima and improves learning speed and paralysis than the conventional error backpropagation learning algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.52-58
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• 1995

A shared-weight neural network that performed classification based on the features extracted with the fuzzy morphological operation is introduced. Learning rules for the structuring elements, degree of membership, and weighting factors are also precisely described. In application to handwritten digit recognition problem, the fuzzy morphological shared-weight neural network produced the results which are comparable to the state-of-art for this problem.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.138-140
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• 2005

A fuzzy ARTMAP neural network and a fuzzy ART neural network are proposed to identify $H_2S$, $NH_3$ and their mixtures and to estimate their concentrations, respectively. Features are extracted from a micro gas sensor array operated in a thermal modulation plan. After dimensions of the features are reduced by a preprocessing scheme, the features are fed into the proposed fuzzy neural networks. By computer simulations, the proposed methods are shown to be fast in learning and accurate in concentration estimating. The results are compared with other methods and discussed.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.114-122
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• 2000

This paper proposes a classification method using an amorphous Prototype to minimize classification error caused by such fixed-Prototype-based methods as Fuzzy C-Means, Nearest Neighborring Classification, FMMCNN, and Fuzzy-ART. For this method, a new fuzzy neural network is introduced, in which a convex polytope is generated or adaptively reshaped to classify the given datum into a proper group. Thus, this method contains a function to classify sequential data set. To show the validity of this method, various numerical experiments including comparison results with FMMCNN are presented

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.223-240
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• 1998

The purpose of this paper is to develop the pattern recognition system with a 'Robust' concept to be applicable to flexible manufacture automation in practice. The 'Robust' concept has four meanings as follows. First, pattern recognition is performed invariantly in case the object to be recognized is translated, scaled, and rotated. Second, it must have strong resistance against noise. Third, the completely learned system is adjusted flexibly regardless of new objects being added. Finally, it has to recognize objects fast. To develop the proposed system, contouring, spectral analysis and Fuzzy ART neural network are used in this study. Contouring and spectral analysis are used in preprocessing stage, and Fuzzy ART is used in object classification stage. Fuzzy ART is an unsupervised neural network for solving the stability-plasticity dilemma.