scholarly journals PATTERN OF TRADING PARTNER SELECTION IN DEPUTIZATION SYSTEMS BASED ON ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 17-27
Author(s):  
Seyed Sina Sharifi ◽  
Alireza Pooya ◽  
Mostafa Kazemi ◽  
Azar Kaffashpoor
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Insurance Company ◽  
Membership Functions ◽  
Business Partner ◽  
Inference System ◽  
Statistical Population ◽  
Mean Error ◽  
The Mean ◽  
Neural Fuzzy

Purpose: The purpose of this study is to develop a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system. Methodology: The present research is applied in terms of purpose and descriptive in terms of the research method. The statistical population of the study, based on the subject of the research, the objectives of the research, and the spatial scope of the research, includes 98 agencies of Parsian Insurance Company in East Azarbaijan Province. According to the available statistics, the number of agencies of Parsian Insurance Company in East Azarbaijan Province is 98; Given that designed systems require more samples to arrive at the right answer. Therefore, the sample size will be done using the all-count sampling method. A questionnaire was used to collect the data of the input variables and the sales amount of different types of insurance policies was used for the output part. An adaptive neurophysiological system (ANFIS) has been used to analyze the data. Also, to evaluate the performance of each of the designed systems, the characteristics of the mean error squares and the root mean of the mean error squares were used. Main Findings: The research findings show that the best model designed to select a business partner in agency systems is a system with foot membership functions, some repetitions of 30, and two membership functions at each input. Application of Study: The results of this study can be used in agency systems to select business partners. Novelty/Originality:  The novelty of this study is developing a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system.

Compensatory of Adaptive Neural Fuzzy Inference System

2021 ◽  
Author(s):  
Rabah Mellah ◽  
Hocine Khati ◽  
Hand Talem ◽  
Said Guermah
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Traditional Approach ◽  
Membership Functions ◽  
Inference System ◽  
Analysis Of Stability ◽  
Velocity Tracking ◽  
Neural Fuzzy

The traditional approach to fuzzy design is based on knowledge acquired by expert operators formulated into rules. However, operators may not be able to translate their knowledge and experience into a fuzzy logic controller. In addition, most adaptive fuzzy controllers present difficulties in determining appropriate fuzzy rules and appropriate membership functions. This chapter presents adaptive neural-fuzzy controller equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a two degree of freedom robotic system. The simulation results obtained show a fairly high accuracy in terms of position and velocity tracking, what highlights the effectiveness of the proposed controllers.

scholarly journals Spike discharge prediction based on Neuro-fuzzy system

2017 ◽  
Author(s):  
Mahdi Zarei
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Subtractive Clustering ◽  
Inference System ◽  
Spike Discharge ◽  
Neuro Fuzzy ◽  
Spike Latency ◽  
Neural Fuzzy

AbstractThis paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.

scholarly journals Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence

2021 ◽  
Vol 11 (16) ◽  
pp. 7766
Author(s):  
Dewang Chen ◽  
Jijie Cai ◽  
Yunhu Huang ◽  
Yisheng Lv
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Deep Structure ◽  
High Dimensional Data ◽  
High Accuracy ◽  
Curse Of Dimensionality ◽  
High Dimensional ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neural Fuzzy

Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability.

Analysis and prediction of the effects of temperature extremes on the environment heating in the Central Part of Iran, Iran, Southwest Asia

2020 ◽  
Author(s):  
Vahid Safarianzengir ◽  
behrouz Sobhani ◽  
Mohammadkia Kianian
Keyword(s):  
Fuzzy Inference ◽  
Extreme Temperature ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Inference System ◽  
Mean Error ◽  
Neuro Fuzzy ◽  
The Mean ◽  
Effects Of Temperature ◽  
Training Error

Abstract Investigation of temperature extremes is very important as one of the most important climate parameters in different parts. The purpose of this study is to analyze and predict the temperature extremes in central Iran. Therefore, the minimum and maximum data of 15 synoptic stations in the study area for the period (1988–2018) using Hybrid Artificial Neural Network (HANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used. Finally, Multi-Criteria Decision-Making (MCDM) models TOSIS and SAW were used to prioritize the areas exposed to rising temperature. The results showed that according to ANFIS modeling for predicting extreme temperatures, the lowest mean training error and the mean error of validation for the minimum temperature was equal 0.10 for Yazd Station and 1.66% for Damghan Station. The lowest mean training error and the mean error of validation for the maximum extreme temperature obtained 0.016 for Garmsar Station and 9.39% for Shahroud station. The maximum extreme temperature of two stations of Garmsar and Bafgh (1 and 0.9689, respectively) were more exposed to extreme temperatures based on the TOPSIS model. Garmsar and Salafchegan Stations (1 and 0.9873, respectively) were more exposed to extreme temperatures based on the SAW model.

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

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.

Sign in / Sign up

Export Citation Format

Share Document