A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Cavallaro, Fausto

doi:10.3390/su70912359

Cited by 75 publications

(36 citation statements)

References 35 publications

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“…In simple terms, a FIS is a system which can obtain new knowledge from existing knowledge by using fuzzy logic (Camastra et al 2015;Cavallaro 2015). A fuzzy inference system is made up of three sections: the first section is the fuzzification process when all crisp values are converted to a linguistic input value using a MF of the system (Tahmasebi and Hezarkhani 2012).…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…Moreover, most of the problems do not need rigid conditions in their relative factors which are introduced to the model as input data (Wang et al 2011). The difference between the Mamdani and TKS models were explained by Cavallaro (2015). The main reason for using the Takagi and Sugeno model in this study is that it is a linear combination of inputs and has fuzzy inputs and crisp outputs (Naderloo et al 2017).…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

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A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

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Section: Fuzzy Inference Systemmentioning

confidence: 99%

Section: Fuzzy Inference Systemmentioning

confidence: 99%

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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“…Houshyar et al [11] used a combination of geographic information system (GIS), analytical hierarchy process (AHP), and fuzzy for the assessment of the sustainability of silage corn production in Fars province. Cavallaro [12] used Takagi-Sugeno fuzzy inference in developing a synthetic index for the sustainability assessment of production of the biomass for energy purposes. Zhao and Li [13] proposed a hybrid framework for the evaluation of the Strong Smart Grid (SSG) performance using AHP and technique for order of preference by similarity to ideal solution (TOPSIS).…”

Section: Introductionmentioning

confidence: 99%

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

et al. 2018

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Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries' sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries' sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries' sustainability performance.

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“…Logic allows the representation of uncertain information and the manipulation of this information using fuzzy rules. An expert system with fuzzy inference makes it possible to manipulate variables at the input in correspondence with the output variable (Fausto, 2015). A fuzzy logic system introduces human expert knowledge.…”

Section: Fuzzy Logic Modelingmentioning

confidence: 99%

Pesticides in Drinking Water and Public Health: Modeling Using Artificial Intelligence Techniques

Benzidane¹,

Saddek²,

Mohamed³

et al. 2017

IJSRES

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Abstract. The causes of chronic diseases, including cancers, are extremely complex. Throughout their lives, individuals who differ in genetic makeup and sensitivity are exposed to a wide variety of carcinogens. Some chemicals by themselves are safe but can act as synergists or promoters together with other chemicals to cause the disease. The problem of the harmful effects of pesticides is the long-term consequences. If the acute effects are well known, the long-term projections remain to be elucidated. Surface water has a direct influence on the degree of contamination by pesticides in groundwater. These are contaminated by significant infiltrations. In order to establish a direct relationship between the possible chronic effects of pesticides on public health, it is essential to consider a set of factors that intervene in the process. Some factors are related to climatic conditions, others to the nature of the soil and others to the nature of the pesticide used. Other factors are related to the people who consume these waters in terms of period, age, sex ... etc. Making a classical mathematical analysis becomes very difficult and even impossible. We propose in this study the analysis of these variables by the techniques of artificial intelligence mainly fuzzy inference. The variables analyzed are considered as fuzzy variables. This makes it possible to take care of these uncertainties and vagueness. A database is established, the resulting algorithm will allow us to read instantly the outcome which expresses the degree of contamination on public health from the introduction of the random variables to the input of the system.

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A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

Cited by 75 publications

References 35 publications

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

Pesticides in Drinking Water and Public Health: Modeling Using Artificial Intelligence Techniques

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