A generalized framework for ANFIS synthesis procedures by clustering techniques

Leonori, Stefano; Martino, Alessio; Luzi, Massimiliano; Mascioli, Fabio Massimo Frattale; Rizzi, Antonello

doi:10.1016/j.asoc.2020.106622

Cited by 13 publications

(3 citation statements)

References 52 publications

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“…The ANFIS, or adaptive network-based fuzzy inference system, is a powerful machine learning technique that uses a training algorithm to adjust the parameters of the system in order to approximate the problem under consideration [22]. ANFIS is a very accurate model, indicated by a fairly large degree of interpretation [23], [24]. This architecture is functionally equivalent to Sugeno's fuzzy rule base model.…”

Section: Learning Algorithms 21 Adaptive Neuro Fuzzy Inference System...mentioning

confidence: 99%

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

Kusumadewi

Rosita

Wahyuni

2023

IJ-AI

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It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.

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Section: Learning Algorithms 21 Adaptive Neuro Fuzzy Inference System...mentioning

confidence: 99%

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

Kusumadewi

Rosita

Wahyuni

2023

IJ-AI

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“…This study uses the cluster sampling approach initialized ANFIS and MF which can take the entire advantage of intrinsic data distribution. The cluster sampling is used to develop ANFIS nearest-neighbourhood and to allow the online www.ijacsa.thesai.org generation of advanced rules by excluding the nearestneighbourhood that is not effective anymore [20,39].…”

Section: ) Cluster Samplingmentioning

confidence: 99%

Data Clutter Reduction in Sampling Technique

Jamalludin¹,

Idrus²,

Idrus³

et al. 2022

IJACSA

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Visualization is a process of converting data into its visual form as such data patterns can be extracted from the data. Data patterns are knowledge hidden behind the data. However, when data is big, it tends to overlap and clutter on visualization which distorts the data patterns. Data is overly crowded on visualization thus, it has become a challenge to extract knowledge patterns. Besides, big data is costly to visualize because it requires expensive hardware facilities due to its size. Moreover, it is timely to plot the data since it takes time for data to render on visualizations. Due to those reasons, there is a need to reduce the size of big datasets and at the same time maintain the data patterns. There are many methods of data reduction, which are preprocessing operations, dimension reduction, compression, network theory, redundancy elimination, data mining, machine learning, data filtering and sampling techniques. However, the commonly used data reduction technique is sampling technique that derives samples from data populations. Thus, sampling technique is chosen as a study for data reduction in this paper. However, the studies are scattered and are not discussed in a single paper. Consequently, the objective of this paper is to collect them in a single paper for further analysis in order to understand them in great detail. To achieve the objective, three interdisciplinary databases which are ACM Digital Library, IEEE Explore and Science Direct have been selected. From the database, a total of 48 studies have been extracted and they are from the years 2017 to 2021. Other than sampling techniques, this paper also seeks information on big data, data visualization, data clutter, and data reduction.

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“…FNN with only one hidden layer (Single-layer Feedforward Neural Networks, SFNN) are typical shallow ANNs which are still widely used in the identification of MISO models involving static data in both regression and classification problems. [19][20][21][22] Among the alternatives of existing neural models, which involve deep networks and other hybrid approaches (Adaptative Neuro-Fuzzy Inference Systems, ANFIS; Fuzzy Neural Network, FNN; Fuzzy Clustering-based Neural Networks, FCNN), [23][24][25] SFNN is the simplest, most straightforward and easy-to-understand option for the development of virtual steady-state analyzers.…”

Section: Introductionmentioning

confidence: 99%

Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

Silva

Fontes

Embiruçu

2023

Macro Reaction Engineering

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This work presents the development and validation of two virtual analyzers (density and Melt Index (MI)) for quality monitoring and control of the final product in an industrial unit of Linear Polyethylene (LPE). Both models are based on Feedforward Neural Networks which are improved through a strategy involving the initial estimation of weights and a constructive algorithm to define the number of hidden units. The initialization strategy is based on linearization of the neural model with only one hidden unit (nonlinear model) and subsequent optimization of this model by maximizing its similarity to the standard linear regression model whose solution is obtained analytically. The Initial Neural Model (INM) is then used as a starting point for a gradual increase in the number of hidden units. In a validation test involving MI and density values collected over 2 years of operation, the neural model is able to predict these properties with mean percentage errors equal to 0.81% (MI) and 0.04% (density) and determination coefficients equal to 0.970 (MI) and 0.983 (density). The population coefficient estimated in all tests involving grade transitions (0.96) shows a strong linear correlation between the proposed model and laboratory measurements.

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A generalized framework for ANFIS synthesis procedures by clustering techniques

Cited by 13 publications

References 52 publications

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

Data Clutter Reduction in Sampling Technique

Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

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