论文信息 - A neuro-fuzzy-GA system architecture for helping the knowledge acquisition process

A neuro-fuzzy-GA system architecture for helping the knowledge acquisition process

The knowledge acquisition process consists on extracting knowledge of a domain expert. This work aims to minimize the intrinsic difficulties of the knowledge acquisition process. For achieve this purpose, all possible rules from the domain expert and a set of example were obtained for a short time interval. The proposed hybrid expert system minimizes the knowledge acquisition difficulties using a new methodology. To build this hybrid architecture, several tools were used: symbolic paradigm, connectionist paradigm, fuzzy logic and genetic algorithm.

M. Noirhomme-Fraiture | L.M. Brasil | F.M. De Azevedo | J.M. Barreto

[1] R. Garcia,et al. A methodology for implementing hybrid expert systems , 1996, Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96).

[2] Lourdes M. Brasil,et al. Cooperation of Symbolic and Connectionist Expert System Techniques to Overcome Difficulties , 1995 .

[3] Chang Chieh Hang,et al. The min-max function differentiation and training of fuzzy neural networks , 1996, IEEE Trans. Neural Networks.

[4] Madan M. Gupta,et al. On the principles of fuzzy neural networks , 1994 .

[5] Fernando Mendes de Azevedo,et al. LEARNING ALGORITHM FOR CONNECTIONIST SYSTEMS , 1997 .

[6] Sankar K. Pal,et al. Logical operation based fuzzy MLP for classification and rule generation , 1994, Neural Networks.

[7] Lourdes Mattos Brasil,et al. TRAINING ALGORITHM FOR NEURO-FUZZY-GA SYSTEMS , 2000 .

[8] Lourdes Mattos Brasil,et al. A Hybrid Expert System Architecture for Medical Diagnosis , 1997, ICANNGA.

[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .

[10] Abraham Kandel,et al. Hybrid Architectures for Intelligent Systems , 1992 .

[11] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .