Implementation of Fuzzy and Adaptive Neuro-Fuzzy Inference Systems in Optimization of Production Inventory Problem

Samodol

2021

Purpose: The modern concepts of contemplating joint dynamics of monetary policy effects on economic growth and its indicators require an indirect approach based on empirical research of mainly financial infrastructure, competitiveness of the financial markets and current economic conditions. Meanwhile, the problems of unemployment and the structure of employment within these concepts are most frequently linked with the polarization of the labor market and two important factors, that is, the effects of growth on unemployment and the fact that technological changes affect the changes in salary ranges. Methodology: By using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the set of data from 1995 to 2016, this paper analyzes these issues through a prism of established balances between the labor and financial markets, i.e., the monetization of economy (M1/GDP), financial development (Loans/GDP) and the share of gross government debt in GDP (government gross debt/GDP). Results: The proposed model suggests that the rate of unemployment is conditioned by the financial cycle and monetary policy (M1/GDP, Loans/GDP), as well as the business cycle and fiscal policy (gross d/BDP) and that a controlled and properly directed level of monetization of the economy (M1/BDP) and financial development measured as Loans/GDP can be “sufficient” for economic growth. Conclusion: Waiting in the “monetary union lobby”, i.e., waiting for the ERM II exchange mechanism can last longer than the set deadlines, leading to the need for Croatian economic policy to optimize monetary and fiscal policy measures in order to increase economic growth and reduce unemployment.

Section: Data Set and Methodologymentioning

confidence: 99%

Using ANFIS in joint dynamics of monetization, financial development, public debt and unemployment analysis

Samodol

2021

“…The ANFIS structure is similar to the FIS structure, and the difference is in determining the parameters of membership functions and FIS rules. According to Aleem [2], one of the ANFIS structures is presented in Figure 2. The first step in ANFIS modeling was to initialize the fuzzy inference system that best models the application data.…”

Section: Adaptive Neuro Fuzzy Infererence System (Anfis)mentioning

confidence: 99%

“…The different way could do this step, the first way was to initialize the FIS parameter from preferences, and this method depended on the experience about the distribution of the data set. Another way was to let ANFIS do this with a grid partition or with clustering techniques [2].…”

Section: Adaptive Neuro Fuzzy Infererence System (Anfis)mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach to Raw Material Inventory Control at Pt XYZ

Putri¹,

Djatna²,

Muslich³

2018

IJEMR

Raw material inventory control is important thing for food companies. The adaptive inventory control is able to adapt to changes in the environment, maintain system performance and stability in the face of various problems in the industry, one of which can be applied to controlling raw material inventories. PT XYZ is a jelly drink industry that has a high level of raw material inventory. In 2016 the investment costs incurred by PT XYZ for carrageenan raw materials ranged from 55-566 million IDR. Costs incurred are quite high and require considerable handling in their maintenance. Therefore, a solution is needed to minimize inventory costs without disturbing the business process. The purpose of this study is to analyse the raw materials inventory PT XYZ for jelly drink product. The analysis is carried out on the main raw material, namely agar (carrageenan) which has three suppliers. The method used is BPMN 1.0 and ANFIS (Adaptive Neuro Fuzzy Inference Systems) which form rules of raw material control rules. This method is an alternative decision making and accommodates flexibility in the form of frame work that accommodates uncertainty of information or data that is less accurate. This study uses four input parameters, namely production demand, raw material arrival, usage and stock. The output obtained is in the form of inventory costs. The results of the study are information regarding the role of each actor who stores important data as a sequence of decision making. The application of ANFIS to design a raw material inventory control system using epoch 50 produces 90 rules of rules with testing errors. The average test for the training dataset is 0,00077412 and the test and examination dataset is 0,0006903. Rules of rules obtained can be applied to control raw material inventories.

Jundishapur J Health Sci

“…The purpose of FIS frameworks is to design an input space by applying fuzzy logic (34). FISs have been used in various fields, including urban planning (34), industrial areas (35,36), and natural management (37,38). The RIAM method is a new tool for the execution of EIA (25).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Assessing the Effects of Industrial Complexes on the Environment Using a Fuzzy Expert System

Ghobadi

Ahmadipari

2022

Background: Identifying the effects of industrial complexes on the environmental components is essential in the industrial activities’ development planning. Objectives: The purpose of this study was to evaluate the environmental impact of Khorramabad industrial parks using hybrid modeling. Methods: In the study, a fuzzy expert system was used to evaluate the environmental impact of industrial parks. In the proposed model, a fuzzy inference system (FIS) was designed based on the Mamdani method and rapid impact assessment matrix (RIAM) model. Results: The most negative effects are observed in the –E (major negative impacts) and –C (moderately negative impacts) ranges. About 24% of the negative effects of activities of industrial park 1 are in the –E range. In industrial park 3, the most negative effects are observed in the –C range (27%). Conclusions: Industrial parks cause negative impacts on different parts of the environment. These consequences are a major issue for environmental planners and managers. The proposed approach could increase the accuracy and flexibility of effects in decision-making.