• Journal of the Korean Data and Information Science Society
• /
• v.16 no.2
• /
• pp.445-449
• /
• 2005

Yen et al. [Fuzzy Sets and Systems 106 (1999) 167-177] calculated the fuzzy membership function for the output to find the non-symmetric triangular fuzzy number coefficients of a linear regression model for all given input-output data sets. In this note, we show that the result they obtained in their paper is invalid.

• Proceedings of the KIEE Conference
• /
• 1998.11b
• /
• pp.505-507
• /
• 1998

In this paper, a simple hierarchical fuzzy inference system using structured Takagi-Sugeno type fuzzy inference units(SFIUs) is proposed. The number of fuzzy rules of the proposed HFIS is minimum in the sense of that only the number of partitions of each system variables, not of intermediate outputs of layered fuzzy controllers, are concerned. And resulted number of fuzzy rules is a summation of partition in each system variables. Gradient descent algorithm is used for adaptation of fuzzy rules. The ball and beam control is performed in computer simulation to illustrate the performance of the proposed controller.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.913-918
• /
• 2006

Chanas(2001) introduced the notion of interval approximation of a fuzzy number with the condition that the width of this interval is equal to the width of the expected interval. In this note, this condition is relaxed and the resulting formulae are derived for determining the approximation interval. This interval is compared with the expected interval and approximation interval of a fuzzy number as introduced by Chanas.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.358-363
• /
• 1998

An algorithm for representing the cubic spline interpolation of differentiable functions by a fuzzy system is presented in this paper. The cubic B-spline functions which form a basis for the interpolation function are used as the fuzzy sets for input fuzzification. The ordinal number of the coefficient cKL in the list of the coefficient cij's as sorted in increasing order, is taken to be the output fuzzy set number in the (k, l) th entry of the fuzzy rule table. Spike functions are used for the output fuzzy sets, with cij's as support boundaries after they are sorted. An algorithm to compute the support boundaries explicitly without solving the matrix equation involved is included, along with a few properties of the fuzzy rule matrix for the designed fuzzy system.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.3-5
• /
• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Journal of the Korea Society for Simulation
• /
• v.18 no.1
• /
• pp.17-25
• /
• 2009

This paper suggests fuzzy-based Segment-Boost method and an effective method for face recognition using the fuzzy-based Segment-Boost. Fuzzy-based Segment-Boost eliminates the limitations of Segment-Boost, and it guarantees improved learning performance and the stability of the performance. By using the fuzzy theory, fuzzy-based Segment-Boost optimizes the selection number of sub-vectors, and leads the optimized learning performance. The fuzzy controller designed in this paper measures learning performance of the fuzzy-based Segment-Boost, and it controls the selection number of sub-vectors by inferring the optimized selection number. The simulation results show that the fuzzy controller inferred the selection number which is very approximate to the true optimized value. As a result, fuzzy-based Segment-Boost showed higher face recognition rate than compared boosting methods and it preserves the velocity of feature selection as fast as that of Segment-Boost. From the experimental results, it was proved that fuzzy-based Segment-Boost has improved and stable performances of learning, feature selection and face recognition.

• Journal of Information Processing Systems
• /
• v.11 no.2
• /
• pp.184-204
• /
• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.1
• /
• pp.40-44
• /
• 2004

In this paper we study the existence and uniqueness of fuzzy solutions for the nonlinear fuzzy integro-differential equations on $E^{n}_{N}$ by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in $E^{n}_{N}$. $E^{n}_{N}$ be the set of all fuzzy numbers in $R^{n}$ with edges having bases parallel to axis $x_1$, $x_2$, …, $x_n$.

• Korean Journal of Mathematics
• /
• v.4 no.2
• /
• pp.117-124
• /
• 1996

In this paper we define a new fuzzy metric $\tilde{\theta}$ of fuzzy number sequences, and prove that the space of convergent sequences of fuzzy numbers is a fuzzy complete metric space in the fuzzy metric $\tilde{\theta}$.

• Journal of applied mathematics & informatics
• /
• v.6 no.1
• /
• pp.265-272
• /
• 1999

In this paper we define generalized concepts of cardinal-ity of a fuzzy-valued function and obtained some properties of these new concepts.