• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• The Journal of the Korean dental association
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• v.15 no.12
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• pp.957-962
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• 1977

The author has observed the tissue reaction of the absolute alcohol infection of rat oral mucosa. 0.5ml absolute alcohol was injected subcutaneously on the mucobuccal fold of rat. And the rats were sacrifieced at intervals of one day, 3rd, 1 week, 2 week and 4 week after alcohol injection. The microscopic tissue sections were made and stained with hematoxylin and eosin. The results were are as follows; 1. Degeneration and shrinkage of fibroblasts and coagulative necrosis were observed one day to and three day after alcohol injection. 2. Although coagulative necrosis and tissue degeneration occurred, the inflammatory infiltration was not prominent especially there were scarcely any polymorphonuclear leukocytes in that field. 3. Granulation tissue with moderate small round cell infiltration were replaced the necrotic area at one week after injection and the fibroblast proliferate into the granulation tissue at two week group. 4. At four week after injection, the damaged area recovered by fibroblastic proliferation and collage formation, but there were

• Journal of Pharmaceutical Investigation
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• v.8 no.1
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• pp.27-36
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• 1978

Spherical granules of anti-inflammatory enzyme were prepared by mixer granulation and the recovery rate of enzyme activity by processing was compared with tablet ones. In the enteric granule coating .processing, the effect of the amount of coating solution and the conentration of fatty alcohol on disintegration and stabilities on the accelerated conditions were also studied. Being prepared in non-pressure and non-aqueous condition, spherical granules of enzyme made better recovery of enzme activity than tablet ones by 10 times. Combined processing of both mixer granulation and enteric granule film coating provided the noble enteric coated granules, in the sense of disintegration and stabilities, was obtained from using 0.125% fatty alcohol in coating solution.

• 한국문화재보존과학회:학술대회논문집
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• 2003.03a
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• pp.8-9
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• 2003

• 한국전기화학회:학술대회논문집
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• 2002.07a
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• pp.265-270
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• 2002

• Journal of Pharmaceutical Investigation
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• v.20 no.3
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• pp.1-24
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• 1990

• Proceedings of the KSMRM Conference
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• 1999.11a
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• pp.85-85
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• 1999

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.5
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• pp.290-295
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• 2002

Fertilizer granulation test was carried out by a small pan granulator. A premixture composed of SOP 60%, urea 22% and RP powder 18% was rolled in the pan granulator while 10% phosphoric acid solution(binder) was sprayed on the rolling powder bed. Granules were developed very fast along with a little amount of binder. Hardness, brittle ratio in water and hygroscopicity of granules were improved enough to evaluate physical properties of the fertilizer. Growth responses of tobacco plant to the fertilizer were investigated at seedling and flowering stage by pot experiment under plastic film roof. Seedlings showed poor growth at nursery pot cell. In virgin soil with deficient available phosphate tobacco plant showed poor growth until budding and flowering stage but good growth in tillage soil with high cumulative phosphate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.