An investigation of membership functions on performance of ANFIS for solving classification problems

Talpur, Noureen; Salleh, Mohd Najib Mohd; Hussain, Kashif

doi:10.1088/1757-899x/226/1/012103

Cited by 96 publications

(58 citation statements)

References 5 publications

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“…In this study, the performance of four membership functions-trapezoidal, triangular, Gauss- ian and generalized bell-shaped function-was compared. Information regarding the shapes of membership functions can be found in the paper of Talpur et al (2017).…”

Section: Modelling Approachesmentioning

confidence: 99%

Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning‐Based Models

Kumar

Sihag

2019

Irrigation and Drainage

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The application of several infiltration models in evaluating the infiltration rate of soil is subject to the spatial variability of soil. Infiltration plays an important role in designing and evaluating surface irrigation systems as well as subsurface groundwater recharge systems. The main theme of this paper is to compare the empirical equation‐based models which are used to estimate the infiltration rate of various locations in Kurukshetra, India. Infiltration experiments were conducted at 20 different locations using a mini disc infiltrometer. The least square fitting method was used for each location separately to estimate the equation parameters and infiltration rate of three different empirical models, namely Kostiakov, modified Kostiakov and Novel models. The performance of these empirical infiltration models was further compared with the machine learning‐based adaptive neuro‐fuzzy inference system (ANFIS) and random forest regression (RF) techniques. Statistical performance evaluation parameters (R2, CC, RMSE and MAE) are used for the performance comparison of various models. The Novel infiltration model was observed to be the most reasonable amongst the empirical models tested. However, for machine‐learning methods, the RF approach is observed to be the most appropriate technique for the estimation of the infiltration data. © 2019 John Wiley & Sons, Ltd.

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Section: Modelling Approachesmentioning

confidence: 99%

Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning‐Based Models

Kumar

Sihag

2019

Irrigation and Drainage

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“…In our model, a bell membership function was used to map linguistic parameters to their labels because it is capable of approaching a non-fuzzy set and has a nonzero value at all points. The type of membership function used in mapping linguistic variables to linguistic labels might affect the performance of the system [19,20]. Nevertheless, the current study differs from Anish et al [17] where probability was used to represent the value of each symptom.…”

Section: Discussionmentioning

confidence: 99%

“…Tables 1 and 2 show twenty (20) cases of the postpartum depression dataset and summary of the diagnostics result from the 59 cases, respectively. Figure 1 shows the degree of the clinical symptoms in respect to each case shown in Table 1.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

A neuro fuzzy approach for the diagnosis of postpartum depression disorder

Osubor

Egwali

2018

Iran J Comput Sci

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Postpartum depression is a growing public health problem amongst nursing mothers, which is not given much attention in primary health care settings. It is a type of depression experienced after childbirth that affects an estimated 13-19% of nursing mothers. Postpartum depression is very difficult to diagnose and by concentrating on somatic illnesses, most medical practitioners frequently fail to recognize it. In this paper an Adaptive Neuro Fuzzy Inference System was utilized to predict postpartum depression. Thirty-six data instances were used in training the model. The system had a training error of 7.0706e−005 at epoch 1 and an average testing error of 3.0185. This technique will facilitate the prompt and accurate diagnosis of postpartum depression.

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“…Some of the available clustering choices are grid partitioning, subtractive clustering, fuzzy c-mean clustering, etc. In this case, fuzzy c-mean clustering is chosen for creating the initial FIS because of their superior performance as claimed by many researchers [35]. In ANFIS, there are 2 sets of trainable parameters available in the form of antecedent part parameters and consequent part parameters.…”

Section: Anfis and Pso-anfis Modelmentioning

confidence: 99%

Runoff estimation using modified adaptive neuro-fuzzy inference system

Nath

Mthethwa

Saha

2019

Environmental Engineering Research

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Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

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An investigation of membership functions on performance of ANFIS for solving classification problems

Cited by 96 publications

References 5 publications

Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning‐Based Models

Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning‐Based Models

A neuro fuzzy approach for the diagnosis of postpartum depression disorder

Runoff estimation using modified adaptive neuro-fuzzy inference system

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