• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.674-682
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• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.3
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• pp.19-26
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• 1993

In this paper, we introduce the notion of H-fuzzy esmitopogenous spaces. In section 1, we give the preliminary definitions and some basic results. In section 2, we show that category HFS of H-fuzzy semitiopogenous spaces and continuous maps between them is topological and cotopological. Using ordinary operations, we characterize coreflective subcatgories and then show that each of Top, Prox, Qunif, and Unig is isomorphic with some coreflective subcategory of HFS. Moreover, we show that sa-HFS is closed under the formation of initial sources in a-HFS, whewe a is a symmetrical elementary operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.550-554
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• 1999

We will define a fuzzy quasi-proximity space and give some examples of it. We show that the family M(X, C) of all fuzzy quasi-proximities on X which induce C is nonempty. Moreover we will study the relationship between the category of fuzzy closure spaces and that of fuzzy quasi-proximity spaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.95-100
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• 1998

A notion of 'fuzzy' convergence of filters on a set is introduced. We show that the collection of fuzzy L-limit spaces forms a cartesian closed topological category and obtain an interesting relationship between the notions of 'fuzzy' convergence structure and convergence approach spaces.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.367-369
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• 1997

We introduce a notion of fuzzy L-convergence of filters and show that the collection of fuzzy L-convergence spaces forms a cartesian closed topological category.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.116-119
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• 2005

In this paper, we show that the category of intuitionistic fuzzy proximity spaces has an initial structure, and consequently a subspace structure and a product space structure of them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.225-230
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• 1998

In this paper, we introduce the concepts of intuitionistic fuzzy points and intuitionistic fuzzy neighborhoods. We investigate properties of continuous, open and closed maps in the intuitionistic fuzzy topological spaces, and show that the category of Chang's fuzzy topological spaces is a bireflective full subcategory of that of intuitionistic fuzzy topogical spaces.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.486-492
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• 2003

The vigilance threshold of conventional fuzzy ART algorithm decide whether to permit the mismatch between any input pattern and stored pattern. If the vigilance threshold was large, despite of little difference among input and stored patterns, the input pattern may be classified to new category. On the other hand, if the vigilance threshold was small, the similarity between two patterns may be accepted in spite of lots of difference and the input pattern are classified to category of the stored pattern. Therefore, the vigilance threshold for the image recognition must be experientially set for the good result. Moreover, it may occur in the fuzzy ART algorithm that the information of stored patterns is lost in the weight-adjusting process and the rate of pattern recognition is dropped. In this paper, I proposed the enhanced fuzzy ART algorithm that supports the dynamical setting of the vigilance threshold using the generalized intersection operator of fuzzy logic and the weight value being adaptively set in proportional to the current weight change and the previous weight by reflecting the frequency of the selection of winner node. For the performance evaluation of the proposed method, we applied to the recognition of container identifiers from shipping container images. The experiment showed that the proposed method produced fewer clusters than conventional ART2 and fuzzy ART algorithm. and had tile higher recognition rate.