정보 입자화와 유전자 알고리즘에 기반한 자기구성 퍼지 다항식 뉴럴네트워크의 새로운 접근 = A New Approach of Self-Organizing Fuzzy Polynomial Neural Networks Based on Information Granulation and Genetic Algorithms

다국어 초록 (Multilingual Abstract)

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data (gas furnace process and NOx process data).

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 자기구성 퍼지 다항식 뉴럴네트워크의 구조
• 3. SOFPNN의 최적화를 위한 IG 방법과 유전자 알고리즘
• 4. Information granulation 기반 진화론적 최적 자기구성 퍼지 다항식 뉴럴네트워크 알고리즘과 설계
• 5. 시뮬레이션 및 결과고찰
• 6. 결론
• 감사의 글
• 참고문헌
• 저자소개

참고문헌 (Reference)

1 오성권, "하이브리드 퍼지추론시스템 국제 저널 논문집" 내하 출판사 2005.10

2 오성권, "프로그래밍에 의한 하이브리드 퍼지추론 시스템" 내하 출판사 2005.10.

3 오성권, "프로그래밍에 의한 컴퓨터지능" 내하 출판사 2002.9.

4 박호성, "퍼지 뉴럴 네트워크 구조로의 새로운 모델링 연구" 7 (7): 664-673, 2001.8.

5 박호성, "경쟁적 퍼지다항식 뉴런에 기초한 고급 자기구성 뉴럴네트워크" 53D (53D): 135-144, March,2004

6 S. K. Oh, "The design of self-organizing Polynomial Neural Networks" 141 : 237-258, 2002.

7 A. G. Ivakhnenko, "The Review of Problems Solvable by Algorithms of the Group Method of Data Handling" Pattern Recognition and Image Analysis 5 (5): 527-535, 1995.

8 R. Moore, "Reliability in Computing" Academic Press 1988.

9 G. Vachtsevanos, "Prediction of Gas Turbine NOx Emissions using Polynomial Neural Network" Georgia Institute of Technology 1995.

10 A. G. Ivakhnenko, "Polynomial theory of complex systems" 364-378, 1971.

11 S. K. Oh, "Polynomial Neural Networks Architecture: Analysis and Design" 29 : 703-725, 2003.

12 J. C. BEZDEK, "Pattern Recognition with Fuzzy Objective Function Algorithms" Plenum 1981.

13 pages 3-13, "K. De Jong. Are genetic algorithms function optimizers? In Proc. of PPSN II Parallel Problem Solving from Nature" North Holland 1992.

14 S. K. Oh, "Hybrid Identification in Fuzzy-Neural Networks" 138 : 399-426, 2003.

15 W. Pedrycz, "Granular neural networks" 36 : 205-224, 2001

16 D. E. Goldberg, "Genetic Algorithm in search" 1989.

17 W. Pedrycz, "An identification algorithm in fuzzy relational system" 13 : 153-167, 1984.

18 Holland, "Adaptation In Natural and Artificial Systems The University of Michigan Press"

19 Yin Wang, "A self-organizing neural-network-based fuzzy system" 103 : 1-11, 1999.

20 J. Leski, "A new artificial neural networks based fuzzy inference system with moving consequents in if-then rules and selected applications" 108 : 289-297, 1999.

21 Y. Lin, "A new approach to fuzzy-neural modeling" 3 (3): 190-197, 1995.