scholarly journals Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 700 ◽  
Author(s):  
Chan-Uk Yeom ◽  
Keun-Chang Kwak
Keyword(s):  
Performance Comparison ◽  
Combined Cycle ◽  
Subtractive Clustering ◽  
Segmentation Method ◽  
Clustering Method ◽  
Space Partitioning ◽  
Anfis Model ◽  
Inference System ◽  
Input Space ◽  
Fuzzy C Means

In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

scholarly journals Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 484 ◽  
Author(s):  
Stéfano Frizzo Stefenon ◽  
Roberto Zanetti Freire ◽  
Leandro dos Santos Coelho ◽  
Luiz Henrique Meyer ◽  
Rafael Bartnik Grebogi ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Subtractive Clustering ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Electrical Insulator ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

Segmentation of Marrow Cells Images Based on Fuzzy C-Means Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2160-2163
Author(s):  
Guo Qiang Ma ◽  
Xiao Juan Wang
Keyword(s):  
Visual System ◽  
Marrow Cell ◽  
Segmentation Method ◽  
Clustering Method ◽  
Matrix Component ◽  
R Matrix ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Frame Work ◽  
Marrow Cells

When a person watches different marrow-cell images he or she can identify every type of cells easily. In this process, human’s visual system has ability to adapt the different shades of the color marrow cells images. We propose a segmentation method for marrow-cell images based on fuzzy c-means clustering (FCM). Firstly, the count of cluster is calculated out using the shades of the R-matrix of a RGB formatted marrow cells image. Secondly, the fuzzy c-means clustering method is done on the R-matrix. Finally, the pixel of G-matrix and B-matrix are divided into some clusters by “one to one correspondence” of the position of pixels that belong to R-matrix, G-matrix or B-matrix. This paper’s contribution could be summarized into three points: 1) a frame work of the fuzzy c-means clustering for marrow-cell images segmentation is proposed. 2) Using FCM and the R- matrix component of a RGB formatted marrow-cell images to generate the count of clustering. 3) This method could adaption different shades of different marrow-cell images.

Study on ANFIS Application in Coal Mining Stray Current Security Prediction

2010 ◽  
Vol 426-427 ◽  
pp. 216-219
Author(s):  
C.Y. Ma ◽  
D.L. Zhang ◽  
Zhi Wang ◽  
G.X. Li ◽  
J.J. Tang
Keyword(s):  
Coal Mining ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Accuracy Rate ◽  
Stray Current ◽  
Anfis Model ◽  
Inference System ◽  
Practical Project ◽  
Project Data

On basis of analyzing the principles and structure of adaptive neural fuzzy inference system (ANFIS), this thesis used subtractive clustering algorithm to get fuzzy inference rule numbers and confirm the network structure. In addition, the thesis built ANFIS model adapted to coal mining workface stray current security prediction. The model can do workface stray current security prediction by the easy measured parameters of non-production field. If the stray current exceeds standard, the system will alarm on time. Moreover, the thesis compared accuracy rate of the security prediction results under different membership functions. The results indicate that the prediction accuracy of ANFIS based on subtractive clustering is the highest and its computing speed is faster. The prediction results to practical project data indicate that stray current security prediction based on ANFIS has favorable practicality and effect.

scholarly journals A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
C. K. Kwong ◽  
K. Y. Fung ◽  
Huimin Jiang ◽  
K. Y. Chan ◽  
Kin Wai Michael Siu
Keyword(s):  
Customer Satisfaction ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Computational Effort ◽  
Subtractive Clustering ◽  
Fuzzy Approach ◽  
Anfis Model ◽  
Inference System ◽  
Affective Design ◽  
Neural Fuzzy

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzyc-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

scholarly journals Simultaneous optimization of clustering and fuzzy IF-THEN rules parameters by the genetic algorithm in fuzzy inference system-based wave predictor models

2017 ◽  
Vol 19 (3) ◽  
pp. 385-404 ◽  
Author(s):  
Morteza Zanganeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference System ◽  
Lake Michigan ◽  
Fuzzy Inference ◽  
Simultaneous Optimization ◽  
Subtractive Clustering ◽  
Generation Process ◽  
Anfis Model ◽  
Inference System ◽  
Wave Parameters

Prediction of wave parameters is of great importance in the design of marine structures. In this paper, two shortcomings with the adaptive network-based fuzzy inference system (ANFIS) model for prediction of wave parameters are remedied by employing a genetic algorithm (GA). The first shortcoming in the ANFIS model goes back to its problem for automatic extraction of fuzzy IF-THEN rules and the second one is related to its gradient-based nature for tuning the antecedent and consequent parameters of fuzzy IF-THEN rules. To deal with these shortcomings, in this study a combined FIS and GA model is developed in which the capability of the GA as an evolutionary algorithm is used for simultaneous optimization of the subtractive clustering parameters and the antecedent and consequent parameters of fuzzy IF-THEN rules. Following the development of the combined model, this model is used to predict wave parameters, i.e., significant wave height and peak spectral period at Lake Michigan. The obtained results show that the developed model outperforms the ANFIS model and the Coastal Engineering Manual (CEM) method to estimate the function representing the generation process of the wind-driven waves.

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