scholarly journals KNOWLEDGE-BASED FIS AND ANFIS MODELS DEVELOPMENT AND COMPARISON FOR RESIDENTIAL REAL ESTATE VALUATION

2018 ◽  
Vol 22 (2) ◽  
pp. 110-118 ◽  
Author(s):  
Sukran YALPIR ◽  
Gulgun OZKAN
Keyword(s):  
Real Estate ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Inference System ◽  
Market Values ◽  
Knowledge Based ◽  
Real Estate Valuation

There has been an increasing concern on the development of alternative approaches to overcome the problems and deficiencies that occur during the application of real-estate valuation methods. This study was established to investigate the usability of the expert knowledge based fuzzy logic methodology in determining real-estates values. In addition, valuation with the Adaptive Neuro-Fuzzy Inference System (ANFIS) method provided model comparison. Samples were administered a questionnaire for the parameters planned for these models regarding the parameters that affect real estate values. To make value estimations for the Fuzzy Inference System (FIS) model by using the parameters obtained from the questionnaire analyses, the criteria that produced the best results were acquired from the various criteria alternatives. An algorithm was created and the valuation process for real estate was performed using the FIS in Konya/Turkey. As a result of poll studies the area, age, floor conditions, physical properties and location of the real-estate property were considered as the input variables and the market value as the output variable. The memberships were established with poll analysis and were rule based on expert knowledge. The model structure was formed by using the Mamdani structure in the MATLAB fuzzy toolbox. Model prediction performance was evaluated statistically with the Mean Absolute Percentage Error (MAPE) and a high accuracy of the model results to the market values indicated the reliability of the established model for residential real-estate valuation.

scholarly journals Algorithm Apriori Association Rule in Determination of Fuzzy Rule Based on Comparison of Fuzzy Inference System (FIS) Mamdani Method and Sugeno Method

2016 ◽  
Vol 7 (1) ◽  
pp. 103
Author(s):  
Muhammad Fadli Arif ◽  
Bima Anoraga ◽  
Samingun Handoyo ◽  
Harisaweni Nasir
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Percentage Error ◽  
Inference System ◽  
Rule Based ◽  
Accuracy Measurement ◽  
First Order

<p>The economic stability of a country can be determined from the changes in the rate of inflation. Inflation is measured by the annual percentage change in the Consumer Price Index. Since there exists some uncertainties in the inflation data, fuzzy logic is one of the ways to analyse the data. Decisions in fuzzy logic can be made using the fuzzy rule-based inference system. Fuzzy rule-based inference can be obtained from expert knowledge, but the knowledge from the experts on the working of a system is not always available. Therefore, the use of association rules<em> </em>approach could solve the problem. Using three methods of fuzzy inferences; namely the Mamdani Methods, zero-order Sugeno method, and the first-order Sugeno method, this study was carried out to determine which method fits to predict the general monthly inflation data in Indonesia. The Inflation data were derived from the inflation of foodstuff price, <em>X<sub>1</sub></em>; inflation of food, drinks, cigarettes and tobacco prices, <em>X<sub>2</sub></em>; inflation of housing, water, electricity, gas, and fuel prices, <em>X<sub>3</sub></em>; inflation of clothing price, <em>X<sub>4</sub></em>; inflation of health care price, <em>X<sub>5</sub></em>; inflation of education, recreation, and sports prices, <em>X<sub>6</sub></em>; and inflation of transportation, communication, and financial services prices, <em>X<sub>7</sub></em>. The performance of the three methods was compared using mean squared error (MSE) and mean absolute percentage error (MAPE) as the accuracy measurement to establish the best fuzzy inference method that fits the inflation value. It was found that the most appropriate method which generated the most accurate results to fit the fuzzy inference system to the inflation data was the first-order Sugeno method.</p>

Knowledge-based preference learning model for recommender system using adaptive neuro-fuzzy inference system

2020 ◽  
Vol 39 (3) ◽  
pp. 4651-4665
Author(s):  
Sunkuru Gopal Krishna Patro ◽  
Brojo Kishore Mishra ◽  
Sanjaya Kumar Panda ◽  
Raghvendra Kumar ◽  
Hoang Viet Long ◽  
...  
Keyword(s):  
Recommender System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Preference Learning ◽  
Data Set ◽  
Inference System ◽  
Knowledge Based ◽  
Main Challenge ◽  
Neuro Fuzzy

A recommender system (RS) delivers personalized suggestions on products based on the interest of a particular user. Content-based filtering (CBF) and collaborative filtering (CF) schemes have been previously used for this task. However, the main challenge in RS is cold start problem (CSP). This originates once a new user joins the system which makes the recommendation task tedious due to the shortage of information (clickstream, dwell time, rating, etc.) regarding the user’s interest. Therefore, CBF and CF are combined together by developing a knowledge-based preference learning (KBPL) system. This system considers the demographic data that includes gender, occupation, and age for the recommendation task. Initially, the dataset is clustered using the self-organizing map (SOM) technique, then the high dimensional data is decomposed by higher-order singular value decomposition (HOSVD) and finally, Adaptive neuro-fuzzy inference system (ANFIS) predicts the output. For the big dataset, SOM is a robust clustering method and the similarities among the users can be easily observed by grid clustering. The HOSVD extracts the required information from the available data set to find the user similarity by decomposing the dataset in lower dimensions. ANFIS uses IF-THEN rules to recommend similar product to the new users. The proposed KBPL system is evaluated with the Black Friday dataset and the obtained error value is compared with the existing CF and CBF techniques. The proposed KBPL system has obtained root mean squared error (RMSE) of 0.71%, mean absolute error (MAE) of 0.54%, and mean absolute percentage error (MAPE) of 37%. Overall, the outcome of the comparative analysis shows minimum error and better performance in terms of precision, recall, and f-measure for the proposed KBPL system compared to the existing techniques and therefore more suitable for accurately recommending the products for the new users.

scholarly journals A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Liang-Cheng Chang ◽  
Hone-Jay Chu ◽  
Yi-Wen Chen
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Water System ◽  
Conjunctive Use ◽  
Subsurface Water ◽  
Inference System ◽  
Resources Management

This study develops the water resources management model for conjunctive use of surface and subsurface water using a fuzzy inference system (FIS). The study applies the FIS to allocate the demands of surface and subsurface water. Subsequently, water allocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface water system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is that the more abundant water system supplies more water. By using the fuzzy rule, the FIS conjunctive use model easily incorporates expert knowledge and operational polices into water resources management. The result indicates that the FIS model is more effective and efficient when compared with the decoupled conjunctive use and simulation-optimization models. Furthermore, the FIS model is an alternative way to obtain the conjunctive use policies between surface and subsurface water.

scholarly journals Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

2015 ◽  
Vol 8 (1) ◽  
pp. 369-384 ◽  
Author(s):  
K. Ramesh ◽  
A. P. Kesarkar ◽  
J. Bhate ◽  
M. Venkat Ratnam ◽  
A. Jayaraman
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Microwave Radiometer ◽  
Percentage Error ◽  
Computational Techniques ◽  
Temperature Profiles ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Temperature And Humidity ◽  
Rainy Days

Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

scholarly journals Fuzzy Inference System Mamdani dan the Mean Absolute Percentage Error (MAPE) untuk Prediksi Permintaan Dompet Pulsa pada XL Axiata Depok

2017 ◽  
Vol 2 (2) ◽  
pp. 97
Author(s):  
Mochammad Bagoes Satria Junianto
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Inference System ◽  
Absolute Percentage Error ◽  
Sample Data ◽  
The Mean

Kemajuan perkembangan teknologi informasi pada era globalisasi sekarang ini sangat pesat; hal ini menuntut setiap perusahaan untuk dapat saling bersaing dalam dunia bisnis yang dinamis dan penuh persaingan. Pada proses manjaemen permintaan dompet pulsa di XL Axiata cabang Depok memerlukan peramalan yang cukup matang agar dompet pulsa yang diminta kepada pusat tidak berlebihan atau tidak terlalu sedikit untuk menjaga kestabilan antara penjualan; persediaan dan jumlah permintaan. Untuk dapat melakukan peramalan yang lebih akurat; maka diperlukan suatu metode yang dapat menghitung ketidakpastian yang terjadi; dalam hal ini metode yang digunakan adalah dengan menggunakan Fuzzy inference system metode Mamdani untuk meramalkan jumlah permintaan dompet pulsa berdasarkan jumlah penjualan dan persediaan. Dengan 12 sample data untuk masing-masing sistem satuam yang digunakan hasil yang didapatkan yaitu dengan menggunakan Fuzzy inference system metode mamdani MAPE yang didapat sebesar 18;56% untuk Dompul XL 5k; 5;38% untuk Dompul XL 10k dan 14;2% untuk Dompul XL Rupiah.

scholarly journals PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS)

2018 ◽  
Vol 10 (1) ◽  
pp. 42-48
Author(s):  
Yampi R. Kaesmetan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Fuzzy Inference ◽  
Rice Production ◽  
Percentage Error ◽  
Centroid Method ◽  
Inference System ◽  
Rice Yields

Rice (Oryza sativa) is a staple food source for the people of Indonesia. Most of the rice consumed is the result of national rice productivity. Often the government has difficulty in estimating the adequacy of basic food items that can be provided by domestic agriculture. Therefore a method is needed to predict rice yields accurately and precisely. The agricultural sector in East Nusa Tenggara is not a flagship of the community's economic activities. This is due to the geographical conditions of NTT which are less supportive for business activities in the agricultural sector. Even so, the prediction of agricultural products, especially rice yields, is needed to be predicted so that a forecast can be obtained in determining rice yields in 2017.  Fuzzy logic method in this case Fuzzy Inference System (FIS) is widely applied for forecasting or prediction. Fuzzy logic has a slowness in predicting crop yields for the following year based on crop yields in the previous year and information taken from the fuzzy information provided. Fuzzyinformation can be made a rule or rule as a consideration in predicting yields. By using the formula of Mean Absolute Percentage Error (MAPE) or Average Absolute Error, from the Fuzzy Mamdani model The Fuzzy Inference System (FIS) with the Mamdani model that has been built can be used to estimate the amount of rice production in the City District in NTT with the truth value reaching 97.8%. To determine the amount of rice production in 2017, the data is processed by using the help of the Matlab 2012 fuzzy toolbox software using the centroid method for defuzzification.

scholarly journals PERANCANGAN MODEL PENDUKUNG KEPUTUSAN PENGADAAN BARANG DENGAN MENGGUNAKAN FUZZY INFERENCE SYSTEM

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Viera Astry ◽  
Dadang Surjasa ◽  
Dedy Sugiarto
Keyword(s):  
Fuzzy Inference System ◽  
Consumer Satisfaction ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Arima Model ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Validation Process ◽  
Inference System ◽  
Verification Process

<p>Alleriea is a small medium enterprises engaged in the field of providing souvenirs. To increase consumer satisfaction, the company should be able to fullfill consumer demand, The decisions support system in this study is using Fuzzy Inference System with Mamdani type as intuitive and very suitable to be given expert knowledge. This model was designed using MATLAB software and as input will be used to predict the number of requests, the speed of supply and stock condition.<br />The predicted number of demand are made by using forecasting methods by selecting a forecasting model with the smallest MSE value. Based on the comparison of the value of MSE on the ARIMA model and winter, forecasting results obtained by the method of Winter has the smallest MSE value.<br />The verification process is done by looking at the forecasting model with the smallest MSE, the validation process is done to test the normality of residual data. The verification process on fuzzy inference systems is done by testing whether the rules given leave in accordance with the desired output. The validation process using a combination of testing Extreme Test uses a combination of extreme in any condition. The result of this paper is a procurement decision support model using fuzzy inference system which influenced by the demand forecast, stock condition and speed of supply. Designed models have been verified and validated.</p>

scholarly journals A Knowledge-based System for Control of the Environment for Single-stem Roses

HortScience ◽  
1996 ◽  
Vol 31 (4) ◽  
pp. 654c-654
Author(s):  
Kuanglin Chao ◽  
Richard S. Gates ◽  
Robert G. Anderson
Keyword(s):  
Fuzzy Inference System ◽  
Crop Production ◽  
Fuzzy Inference ◽  
Knowledge Engineering ◽  
Production Costs ◽  
Economic Optimization ◽  
Inference System ◽  
Knowledge Based ◽  
Conflicting Demands ◽  
Nondestructive Measurements

Knowledge engineering offers substantial opportunities for integrating and managing conflicting demands in greenhouse crop production. A fuzzy inference system was developed to balance conflicting requirements of producing a high-quality, single-stem rose crop while simultaneously controlling production costs of heating and ventilation. An adaptive neuro-fuzzy inference system was built to predict the rose status of `Lady Diana' single-stem roses from nondestructive measurements. The fuzzy inference system was capable of making a critical decision based on the principle of economic optimization. Temperature set points for two greenhouses with similar rose status were treated significantly different by the fuzzy inference system due to differences in greenhouse energy consumption. Moderate reduction in heating energy costs could be realized with the fuzzy inference system.

scholarly journals Perancangan Sistem Pendukung Keputusan Penentuan Impor Bawang Merah

2015 ◽  
Vol 2 (3) ◽  
pp. 181
Author(s):  
Wiwi Widayani ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Market Price ◽  
Training Data ◽  
Percentage Error ◽  
Inference System ◽  
Absolute Percentage Error ◽  
Testing Data ◽  
The Government

Pertambahan jumlah penduduk Indonesia serta meningkatkannya permintaan industri akan bawang merah yang tidak diimbangi dengan jumlah produksi mendorong pemerintah membuka impor bawang merah. Impor dilakukan untuk menjaga keseimbangan harga dan pasokan bawang merah sehingga inflasi yang diakibatkan kenaikan harga bawang merah dapat ditekan, namun impor yang tidak tepat jumlah akan mengakibatkan kerugian bagi pihak petani, perlu adanya sistem pendukung dalam menentukan volume impor guna menjaga keseimbangan harga pasar dan pemenuhan kebutuhan bawang merah. Sistem pendukung keputusan yang dirancang menerapkan Fuzzy Inference System (FIS) Tsukamoto. Sistem yang dirancang memungkinkan pengguna untuk melakukan training data dan testing data, proses dalam training data yaitu : 1)Clustering data latih, menggunakan algoritma K-Means 2)Ekstraksi Aturan, 3)Testing data latih, hitung nilai impor dengan fuzzy Tsukamoto, 4)Menganalisa error hasil fuzzy menggunakan MAPE(Means Absolute Percentage Error), 5)Testing Data Uji dan menganalisa hasil error data uji. Hasil Uji Model menunjukan penentuan impor bawang merah dengan parameter input harga petani, harga konsumen, produksi, konsumsi, harga impor dan kurs terhadap 60 data latih menghasilkan error terendah sebesar 0.07 pada 12 cluster, hasil uji mesin inferensi terhadap data uji menghasilkan error sebesar 0.25. Indonesian population growth and increase industrial demand shallot is not matched with number of production prompted the government to opened shallot imports. Import done to maintain the balance price and supply of shallot so inflation caused by rising prices of onion can be suppressed, but not the exact amount of imports would result in losses for the farmers, support system in determining volume imports is need to maintain balance of market price and needs of shallot. Decision support system designed to apply Fuzzy Inference System (FIS) Tsukamoto. The system is allows the user to perform the training data and testing data, the training process performs are: 1) Clustering training data, using the K-Means algorithm 2) Extraction Rule, 3) Testing data, calculate imports value by fuzzy Tsukamoto, 4) analyze the results error using MAPE (Means Absolute Percentage error), 5) testing test data and analyze the results error. The results show the determination of imported shallot with input parameters producer prices, consumer prices, production, consumption, import prices and the exchange rate against 60 training data produces the lowest error of 0:07 in 12 clusters, the inference engine test resulted in an error of 0.25.

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