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Design of Softmax Function-based RBFNN Classifier Realized with the Aid of Optimized Fuzzy Transform
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최적화된 Fuzzy Transform을 이용한 소프트맥스 함수 기반 RBFNN 분류기 설계

논문 기본 정보

Type
Academic journal
Author
Sang-Beom Park (수원대학교) Sung-Kwun Oh (수원대학교) Hyun-Ki Kim (수원대학교)
Journal
Korean Institute of Intelligent Systems Journal of Korean Institute of Intelligent Systems Vol.28 No.2 KCI Accredited Journals
Published
2018.4
Pages
99 - 106 (8page)
DOI
10.5391/JKIIS.2018.28.2.99

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Design of Softmax Function-based RBFNN Classifier Realized with the Aid of Optimized Fuzzy Transform
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Abstract· Keywords

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In this paper, softmax function-based RBFNN classifier wth the optimized fuzzy transform is introduced and the excellence of the proposed classifier is demonstrated through several experiments. In addition, differential evolution(DE) and particle swarm optimization(PSO) are used to optimize the introduced classifier. Furthermore, the shapes of the optimized membership functions, whose center points are optimized by using DE and PSO have been compared. The reduced input variables obtained by using the optimized fuzzy transform are used as the inputs of a softmax function-based RBFNN classifier. The cross entropy error function is used as the cost function and newton method based-nonlinear least square estimation is applied to estimate the coefficients of connection weights. Moreover, softmax function is used as activation function of the output layer in order to normalize the output values between 0 and 1. When output values normalized by softmax function are regarded as probability values, the maximum value among probability value is determined as final output value. The spectrum data acquired from Laser Induced Breakdown Spectroscopy(LIBS) are used to validate the classification performance of the inroduced classifier. The proposed classifier shows the superiority from the view point of classification performancec, omparing the proposed classifier with weka data mining softwaer.
#차분진화 #입자군집 최적화 #소프트맥스 함수 #크로스 엔트로피 오차함수 #비선형 최소자승법 #Differential Evolution(DE) #Particle Swarm Optimization(PSO) #Softmax function #Cross entropy error function #Nonlinear Least Square Estimation(Nonlinear LSE)

Contents

요약
Abstract
1. 서론
2. 최적화 알고리즘을 이용한 Fuzzy Transform 멤버쉽함수의 중심점 최적화
3. 소프트맥스 함수 기반 RBFNN 분류기
4. 실험 및 결과고찰
5. 결론 및 향후 연구방향
References

References (18)

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Irina Perfilieva, “Fuzzy transforms: Theory and applications”, Fuzzy Sets and Systems, Vol. 157, No. 8, pp. 993-1023, 2006. S.-B. Roh, S.-K. Oh and W. Pedrycz, “Identification of Black Plastics Based on Fuzzy RBF Neural Networks: Focused on Data Preprocessing Techniques through Fourier Transform Infrared Radiation”, IEEE Transactions on industrial Informatics, No. 99, 2017(to be published). B.-J. Jeong, S.-C. Lee and S.-K. Oh, “Design of Optimized Radial Basis Function Neural Networks Classifier Using EMC Sensor for Partial Discharge Pattern Recognition”, The Transaction of the KIEE, Vol. 66, No. 9, pp. 1392-1401, 2017. Dadabada Pradeepkumar and Vadlamani Ravi, “Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network”, Applied Soft Computing, Vol. 58, pp. 35-52, 2017. S.-B. Park, J.-S. Bae, S.-K. Oh and H.-K. Kim, “Design of Pattern Classifier for Electrical and Electronic Waste Plastic Devices Using LIBS Spectrometer”, Journal of KIIS, Vol. 26, No. 6, pp. 477-484, 2016.

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UCI(KEPA) : I410-ECN-0101-2018-003-001847387