Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

2018 ◽  
Vol 35 (5) ◽  
pp. 2078-2106 ◽  
Author(s):  
Ehsan Sadrossadat ◽  
Behnam Ghorbani ◽  
Rahimzadeh Oskooei ◽  
Mahdi Kaboutari
Keyword(s):  
Gene Expression ◽  
Bearing Capacity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Inference System ◽  
Indirect Estimation ◽  
Content Type ◽  
Rock Foundations ◽  
Neuro Fuzzy

Purpose This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem. Design/methodology/approach The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study. Findings A new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems. Originality/value The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

Yarn Strength Modelling Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP)

2013 ◽  
Vol 8 (4) ◽  
pp. 155892501300800 ◽  
Author(s):  
A.R. Fallahpour ◽  
A.R. Moghassem
Keyword(s):  
Gene Expression ◽  
Root Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Breaking Strength ◽  
Gene Expression Programming ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

This study compares capabilities of two different modelling methodologies for predicting breaking strength of rotor spun yarns. Forty eight yarn samples were produced considering variations in three drawing frame parameters namely break draft, delivery speed, and distance between back and middle rolls. Several topologies with different architectures were trained to get the best adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) models. Prediction performance of the GEP model was compared with that of ANFIS using root mean square error (RMSE) and correlation coefficient (R2-Value) parameters on the test data. Results show that, the GEP model has a significant priority over the ANFIS model in term of prediction accuracy. The correlation coefficient (R2-value) and root mean square error for the GEP model were 0.87 and 0.35 respectively, while these parameters were 0.48 and 0.53 for the ANFIS model. Also, a mathematical formula was developed with high degree of accuracy using GEP algorithm to predict the breaking strength of the yarns. This advantage is not accessible in the ANFIS model.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

scholarly journals GENECFE-ANFIS: A NEURO-FUZZY INFERENCE SYSTEM TO INFER GENE-GENE INTERACTIONS BASED ON RECOGNITION OF MICROARRAY GENE EXPRESSION PATTERNS

2007 ◽  
Vol 19 (02) ◽  
pp. 71-78 ◽  
Author(s):  
Cheng-Long Chuang ◽  
Chung-Ming Chen ◽  
Grace S. Shieh ◽  
Joe-Air Jiang
Keyword(s):  
Gene Expression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expression Patterns ◽  
Gene Interactions ◽  
Microarray Gene Expression ◽  
Inference System ◽  
Qrt Pcr ◽  
Neuro Fuzzy ◽  
Microarray Gene

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

Measuring maintenance activity effectiveness

2018 ◽  
Vol 24 (4) ◽  
pp. 437-448 ◽  
Author(s):  
Anil Rana ◽  
Emosi V.M. Koroitamana
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Multiple Attribute Decision Making ◽  
Performance Measure ◽  
Inference System ◽  
Content Type ◽  
Maintenance Activity ◽  
Expert Opinions ◽  
Neuro Fuzzy ◽  
Multiple Attribute

Purpose The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system. Design/methodology/approach The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data. Findings The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions. Research limitations/implications In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS. Originality/value The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

Contact positions estimation of sensing structure using adaptive neuro-fuzzy inference system

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 783-796 ◽  
Author(s):  
Dalibor Petkovic ◽  
Mirna Issa ◽  
Nenad D. Pavlovic ◽  
Lena Zentner ◽  
Md Nor Ridzuan Daud ◽  
...  
Keyword(s):  
Carbon Black ◽  
Silicone Rubber ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tactile Sensing ◽  
Inference System ◽  
Content Type ◽  
Sensing Material ◽  
Neuro Fuzzy ◽  
Current Contact

Purpose – Tactile sensing is the process of determining physical properties and events through contact with objects in the world. The purpose of this paper is to establish a novel design of an adaptive neuro-fuzzy inference system (ANFIS) for estimation of contact position of a new tactile sensing structure. Design/methodology/approach – The major task is to investigate implementations of carbon-black-filled silicone rubber for tactile sensation; the silicone rubber is electrically conductive and its resistance changes by loading or unloading strains. Findings – The sensor-elements for the tactile sensing structure were made by press-curing from carbon-black-filled silicone rubber. The experimental results can be used as training and checking data for the ANFIS network. Originality/value – This system is capable to find any change of contact positions and thus indicates state of the current contact location of the tactile sensing structure. The behavior of the use silicone rubber shows strong non-linearity, therefore, the sensor cannot be used for high accurate measurements. The greatest advantage of this sensing material lies in its high elasticity.

scholarly journals Using Adaptive Neuro-Fuzzy Inference System for Classification of Microarray Gene Expression Cancer Profiles

2018 ◽  
Vol 2018 (1) ◽  
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Support Vector ◽  
Microarray Gene Expression ◽  
Inference System ◽  
Neuro Fuzzy

Microarray is a technology that enables simultaneously analysis of thousands of genes in DNA structure depending on the advances in biochemistry. With this technology, it has become possible to diagnose and treat heredity diseases by analyzing thousands of gene expression levels. This study proposes an artificial intelligence method, Adaptive neuro-fuzzy inference system (ANFIS), to classify cancer gene expression profiles. The findings obtained with the proposed ANFIS approach are compared with the results of statistical methods such as Naive Bayes and Support Vector Machines. In conclusion, although the highest average classification performance achieved with ANFIS is 95.56%, the highest performance achieved with statistical methods are found to be 87.65%.

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