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.

Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems

2011 ◽  
Vol 332-334 ◽  
pp. 1505-1510
Author(s):  
Xiao Bo Yang
Keyword(s):  
Network Inference ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Real Data ◽  
Subtractive Clustering ◽  
Fuzzy Inference Systems ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Neural ◽  
Inference Systems

In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability of the proposed method to real data.

An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

2014 ◽  
Vol 501-504 ◽  
pp. 391-394
Author(s):  
Yi Ming Xiang ◽  
Xue Yan Liu ◽  
Gui Xiang Ling ◽  
Bin Du
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Subtractive Clustering ◽  
Forecast Errors ◽  
Frost Heaving ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

scholarly journals Performance prediction of construction projects using soft computing methods

2019 ◽  
Vol 46 (7) ◽  
pp. 609-620 ◽  
Author(s):  
Seyedeh Sara Fanaei ◽  
Osama Moselhi ◽  
Sabah T. Alkass
Keyword(s):  
Performance Prediction ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Performance Indicator ◽  
Relative Weight ◽  
Computing Methods ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Project Data

Key performance indicators (KPIs) evaluate different aspects of projects and are used to determine the health status of projects. While there is considerable work on project quantitative performance prediction, less attention, however, has been directed towards qualitative performance prediction. This paper offers a novel framework for qualitatively measuring and predicting six important construction project KPIs using the neuro-fuzzy technique. Neuro-fuzzy models are developed to map the KPIs of three critical project stages to whole project KPIs. Subtractive clustering is utilized to automatically generate initial fuzzy inference system (FIS) models and the artificial neural network (ANN) technique is used to tune the parameters of the initial FIS models. The relative weight of each KPI is determined using a series of computing methods namely, analytic hierarchy process (AHP) and genetic algorithm (GA), to generate the performance indicator (PI). The developed models are validated with real project data showing that the rate of error is reasonably low. The results show that the AHP method is more accurate when compared to the GA method. This framework can be used in building construction projects to help decision-makers evaluate the performance of their projects.

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.

The Research of Error Compensation for the 3D Surface Measurement

2012 ◽  
Vol 482-484 ◽  
pp. 2192-2196
Author(s):  
Yuan Tian ◽  
Zi Ma ◽  
Peng Li
Keyword(s):  
Measurement Error ◽  
Fuzzy Inference System ◽  
Error Compensation ◽  
Point Cloud ◽  
Fuzzy Inference ◽  
Surface Measurement ◽  
Anfis Model ◽  
Inference System ◽  
Surface Error ◽  
3D Surface

For improving precision of 3D surface measurement equipments, which are playing important role in reverse engineering, the Adaptive Network based Fuzzy Inference System (ANFIS) is developed to reconstruct 3D surface error, and the measurement error of point cloud is compensated by the presented 3D error ANFIS model. The precision of 3D surface measurement equipments has been improved noticeably

scholarly journals Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

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