SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System |
| |
| Authors: | S.W. Tung C. Quek C. Guan |
| |
| Affiliation: | 1. Programa de Doctorado en Ciencias Biológicas y de la Salud, Universidad Autónoma Metropolitana Iztapalapa-Xochimilco–Cuajimalpa, Calzada del Hueso 1100, Col. Villa Quietud, Mexico, D.F. 04960, Mexico;2. Universidad Autónoma Metropolitana, Campus Xochimilco (UAM-X), Stress Physiology and Farm Animal Welfare, Department of Animal Production and Agriculture, D.F. Calzada del Hueso 1100, Col. Villa Quietud, México, D.F. 04960, Mexico;3. Laboratorio de Bioquímica de Macromoléculas, Departamento de Biotecnología, Universidad Autónoma Metropolitana Iztapalapa, Mexico, D.F. 09340, Mexico;4. Universidad Nacional Autónoma de México (UNAM), Department of Livestock Sciences, Facultad de Estudios Superiores Cuautitlán (FESC), Cuautitlán Izcalli, Estado de México 54714, Mexico;5. Universidad del Valle de México, Animal Welfare Area, Licenciatura en Medicina Veterinaria y Zootecnia, Campus Coyoacán, Calzada de Tlalpan No. 3058, Col. Santa Ursula Coapa, Delegación Coyoacán, México, D.F., Mexico;6. División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Av. Universidad s/n, Zona de la Cultura, C.P. 86040 Villahermosa, Tabasco, Mexico;7. Department of Swine Health, Mexico;1. Grupo de Investigación en Telecomunicaciones Aplicadas (GITA), Faculty of Engineering, Department of Electronic Engineering, Universidad de Antioquia (UdeA), Calle 50 No. 73-21, 050034 Medellín, Colombia;2. Faculty of Engineering, Department of System Engineering, Universidad de Antioquia (UdeA), Calle 50 No. 73-21, 050034 Medellín, Colombia;3. Grupo de Investigación en Ingeniería y Software (INGYSOFT), Faculty of Engineering, Department of System Engineering, Universidad de Antioquia (UdeA), Calle 50 No. 73-21, 050034 Medellín, Colombia |
| |
| Abstract: | The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
