On a Learning Method of the SIC Fuzzy Inference Model with Consequent Fuzzy Sets

2019 ◽  
Author(s):  
Genki Ohashi ◽  
Hirosato Seki ◽  
Masahiro Inuiguchi
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Learning Method ◽  
Inference Model

On the Learning Method, Properties of the Extended Functional-Type SIRMs Connected Fuzzy Inference Model and Their Application to a Medical Diagnosis System

2018 ◽  
Vol 22 (2) ◽  
pp. 176-183
Author(s):  
Diederik van Krieken ◽  
Hirosato Seki ◽  
Masahiro Inuiguchi ◽  
◽  
Keyword(s):  
Medical Diagnosis ◽  
Fuzzy Inference ◽  
Learning Method ◽  
Functional Type ◽  
Inference Model ◽  
Diagnosis System ◽  
Single Input ◽  
Medical Diagnosis System

Seki et al. have proposed the functional type single input rule modules fuzzy inference model (functional-type SIRMs model, for short) which generalized consequent part of SIRMs model to function. However, it is too strict to satisfy the equivaence conditions of T–S inference model. Therefore, this paper proposes an extended functional-type SIRMs model (EF-SIRMs, for short) in which the consequent part of the functional-type SIRMs model is extended to a function with 1 dimensional polynomial from a function with n dimensional polynomial, and its properties are clarified. Further, it shows the ability of this model becomes greatly larger than that of ordinary functional-type SIRMs model. Moreover, it proposes a learning method of the EF-SIRMs model, and it is applied to a medical diagnosis, and compared with the conventional SIRMs models.

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

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