PATTERN OF TRADING PARTNER SELECTION IN DEPUTIZATION SYSTEMS BASED ON ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM

Abstract

Purpose: The purpose of this study is to develop a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system. Methodology: The present research is applied in terms of purpose and descriptive in terms of the research method. The statistical population of the study, based on the subject of the research, the objectives of the research, and the spatial scope of the research, includes 98 agencies of Parsian Insurance Company in East Azarbaijan Province. According to the available statistics, the number of agencies of Parsian Insurance Company in East Azarbaijan Province is 98; Given that designed systems require more samples to arrive at the right answer. Therefore, the sample size will be done using the all-count sampling method. A questionnaire was used to collect the data of the input variables and the sales amount of different types of insurance policies was used for the output part. An adaptive neurophysiological system (ANFIS) has been used to analyze the data. Also, to evaluate the performance of each of the designed systems, the characteristics of the mean error squares and the root mean of the mean error squares were used. Main Findings: The research findings show that the best model designed to select a business partner in agency systems is a system with foot membership functions, some repetitions of 30, and two membership functions at each input. Application of Study: The results of this study can be used in agency systems to select business partners. Novelty/Originality:  The novelty of this study is developing a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system.