scholarly journals Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System

2010 ◽  
Vol 42 (1) ◽  
pp. 159-167
Author(s):  
Masumeh Sabet ◽  
Mehdi Naseri ◽  
Hosein Sabet
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Linear Process ◽  
Back Propagation Algorithm ◽  
Littoral Drift ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Sand Drift

Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confirming its consistency with a database of specified physical process.

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

scholarly journals Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

2018 ◽  
pp. 1-9
Author(s):  
Ibrahim Goni ◽  
Christopher U. Ngene ◽  
Manga I. ◽  
Auwal Nata’ala ◽  
Sunday J. Calvin
Keyword(s):  
Fuzzy Inference ◽  
Intelligent System ◽  
Research Work ◽  
Back Propagation ◽  
Night Sweat ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
History Of ◽  
Loss Of Appetite

Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2.   

Developing Adaptive Neuro-Fuzzy Inference System for Controlling the Intelligent Universal Transformers in ADA

2012 ◽  
Vol 433-440 ◽  
pp. 3969-3973
Author(s):  
Maryam Sadeghi ◽  
Majid Gholami
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Voltage Regulation ◽  
Current Control ◽  
Distribution Automation ◽  
Membership Functions ◽  
Back Propagation Algorithm ◽  
Input Output ◽  
Inference System ◽  
Neuro Fuzzy

This approach is carry out for developing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the forthcoming Intelligent Universal Transformer (IUT) in regard of voltages and current control in both input and output stages which is optimized by particle swarm optimization. Current or voltages errors and their time derivative have been considered as the inputs of Nero Fuzzy controller for elaborating the firing angles of converters in IUT basic construction. ANFIS constructed from a fuzzy inference system (FIS) in which the membership function parameters are tuned according to the back propagation algorithm or in conjunction to the least squares method. A neural network maps inputs through input membership functions and associated parameters, and output membership functions and associated parameters to outputs which interprets the input-output map. The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters. Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones. Advanced Distribution Automation (ADA) is the state of art introducing for tomorrows distribution automation with the new invention in management and control. ADA is equipping by the Intelligent Equipment Devices (IED) in which IUT is a key point introducing as an intelligent transformer subjecting for tomorrows distribution automation in the near future. The proposed ANFIS is a control scheme develop for controlling the IUT by bringing the major advantages like harmonic Filtering, voltage regulation, automatic sag correction, energy storage, 48V DC option, three phase outputs in term of one phase input, reliable divers power as 240V 400HZ for communication utilization and two other 240V 60 HZ outputs, dynamic system monitoring and robustness in major disturbances occurred in terms of input and load variation.

scholarly journals Missing Samples Estimation of Synthetic ECG Signals by FCM-based Adaptive Neuro-Fuzzy Inference System (FCMANFIS)

2021 ◽  
Author(s):  
asghar dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoon Nooshirvan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Synthetic Data ◽  
Least Square ◽  
Inference System ◽  
Ecg Signals ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
System File

Abstract This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) signals by an ANFIS (Adaptive neuro-fuzzy inference system) method. After designing the ANFIS model using FCM (Fuzzy C Means) clustering method. In MATLAB’s standard library for ANFIS, only least-square-estimation and the back-propagation algorithms are used for tuning membership functions and generation of fis (fuzzy inference system) file, but at current work we have used FCM method that shows better result. Root mean square error (difference of the reference input and the generated data by ANFIS) for the three synthetic data cases are: a. Train data: RMSE = 1.7112e-5b. Test data: RMSE = 5.184e-3c. All data: RMSE = 2.2663e-3

scholarly journals Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 935 ◽  
Author(s):  
Daniel Teso-Fz-Betoño ◽  
Ekaitz Zulueta ◽  
Unai Fernandez-Gamiz ◽  
Aitor Saenz-Aguirre ◽  
Raquel Martinez
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Path Selection ◽  
Algorithm Performance ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Propagation Technique ◽  
Window Approach ◽  
Neural Fuzzy

The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

Gradient-based back-propagation dynamical iterative learning scheme for the neuro-fuzzy inference system

2015 ◽  
Vol 33 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Hadi Chahkandi Nejad ◽  
Mohsen Farshad ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Omid Khayat
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Iterative Learning ◽  
Inference System ◽  
Learning Scheme ◽  
Neuro Fuzzy ◽  
Gradient Based

The Study of System Model Based on Fuzzy Inference and Neural Network

2012 ◽  
Vol 197 ◽  
pp. 547-552
Author(s):  
Ming Ming Gao ◽  
Liang Shan
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Fuzzy Rules ◽  
System Model ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Anfis Method

For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.

scholarly journals Drug Stock Prediction at Balige HKBP Hospital Using Adaptive Neuro-Fuzzy Inference System

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 26-34
Author(s):  
Arie Satia Dharma ◽  
Lily Andayani Tampubolon ◽  
Daniel Somanta Purba
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Financial Loss ◽  
Stock Prediction ◽  
Least Square Estimator ◽  
Inference System ◽  
Error Back Propagation ◽  
Neuro Fuzzy

Currently the purchases of drugs at Instalasi Farmasi RSU (IFRS) HKBP Balige are based on the examination of the amount of drugs usage. The purchases of drugs based on the examination of the amount of drugs usage cause frequent unplanned drugs purchases that must be hastened (cito) and purchases to other pharmacies. The purchases of cito and purchases to other pharmacies will inflict a financial loss to the patients, because when IFRS makes drugs purchases of cito or to other pharmacies, the cost of the drugs will be more expensive. Therefore, in this research, a prediction of drugs stock in IFRS HKBP Balige using Adaptive Neuro Fuzzy Inference System (ANFIS) will be carried out. ANFIS is a combination of Least Square Estimator (LSE) and Error Back Propagation (EBP) algorithms. ANFIS consists of forward pass and the backward pass learning. The sample data used to predict drugs stock in this research is data of drugs sales at the IFRS HKBP Balige from 2013 to 2015. From the results of drugs stock prediction research with ANFIS, obtained that number of errors of ANFIS model is 5.52%. Based on MAPE accuracy level evaluation, number of errors have an excellent rate so that it can be concluded that the predicted results of the drugs stock are good.

Λήψη αποφάσεων και ασαφής λογική

2020 ◽  
Author(s):  
Ελένη Βλάμου
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Vector Quantization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Inference Systems

Η ασαφής λογική αποτελεί μια θεωρία της οποίας οι εφαρμογές έχουν σκοπό να παρέχουν βελτιωμένες λύσεις σε προβλήματα με υψηλό βαθμό αβεβαιότητας. Η θεωρία, η τεχνολογία και οι εφαρμογές της ασαφούς λογικής έχουν σημειώσει τα τελευταία χρόνια ταχύτατη ανάπτυξη και έχουν καταστεί αξιόπιστο και εύχρηστο εργαλείο σε πολλές επιστημονικές και ερευνητικές περιοχές. H παρούσα διατριβή εστιάζει στην κατανόηση των δομών της ασαφούς λογικής, και στην ανάλυση των ασαφών κανόνων και συστημάτων. Γίνεται μια ολοκληρωμένη παρουσίαση της θεωρίας ασαφών συνόλων με έμφαση στην κατανόηση των ασαφών συστημάτων (Fuzzy Inference Systems). Σκοπός είναι η ανάλυση της αποτελεσματικότητας της εφαρμογής της ασαφούς λογικής σε ποικίλα ασαφή συστήματα. Επιπλέον εστιάζει στην ανάδειξη της σπουδαιότητας της ασαφούς λογικής και των ασαφών συστημάτων λήψεως αποφάσεων, η χρήση των οποίων παρουσιάζει σημαντικά πλεονεκτήματα αναφορικά με την αποτελεσματικότητά τους. Ειδικότερα, η μίξη των τεχνητών νευρωνικών δικτύων και ασαφών συστημάτων επιτρέπει στους ερευνητές να διαμορφώνουν προβλήματα με την ανάπτυξη των έξυπνων και προσαρμοστικών συστημάτων. Έτσι γίνεται η περιγραφή του μοντέλου του ασαφούς νευρωνικού δικτύου και παρουσιάζονται εκπαιδευτικοί αλγόριθμοι (όπως ο Back-Propagation) που χρησιμοποιείται για τη βελτίωση της απόδοσης του δικτύου. Η βελτιωμένη αποδοτικότητα των εκπαιδευμένων ασαφών δικτύων επιβεβαιώνεται με την εφαρμογή και οπτικοποίηση του αλγορίθμου Back-Propagation στην Matlab. Επιπλέον γίνεται ανάλυση της εφαρμογής των ασαφών συστημάτων με σκοπό την αναγνώριση προτύπων και ανάλυση εγκεφαλογραφικού σήματος. Έτσι, γίνεται περιγραφή των γραμμικών μεθόδων αναγνώρισης προτύπων για την ανάλυση του σήματος του εγκέφαλου (όπως οι μετασχηματισμοί Fast Fourier transform, μετασχηματισμός Wavelet και μετασχηματισμός Vector Quantization) με σκοπό την προβολή της υπεροχής των ασαφών νευρωνικών δικτύων (SOMF), των συστημάτων ασαφών ταξινομητών και των ταξινομητών προσαρμοσμένων ασαφών νευρωνικών συστημάτων (ANFIS-Adaptive Neuro-Fuzzy Inference System). Επιπλέον, αναλύεται η εφαρμογή των ασαφών δικτύων αναφορικά με τη διάγνωση της επιδημιολογίας η οποία ενισχύεται με την παρουσίαση διαφορετικών επιδημιολογικών μοντέλων (όπως τα στοχαστικά επιδημιολογικά μοντέλα, π.χ. το μοντέλο SI και SIS, και τα ντετερμινιστικά επιδημιολογικά μοντέλα όπως το μοντέλο SIR) με σκοπό να αναδείξει την υπεροχή των ασαφών μεθόδων σε αυτήν την περίπτωση. Η περιγραφή των ασαφών μοντέλων SI και SIS αναδεικνύει την υπεροχή τους που ενισχύεται με την ανάλυση των ασαφών πιθανοτήτων για τη λήψη αποφάσεων στον τομέα της επιδημιολογίας. Επιπλέον, παρουσιάζεται η εφαρμογή των ασαφών συστημάτων σε θέματα βελτίωσης της απόδοσης των γενετικών αλγορίθμων. Γίνεται η ανάλυση των βασικών αρχών και χαρακτηριστικών των γενετικών αλγορίθμων, η περιγραφή των προσαρμοζόμενων πιθανοτήτων διέλευσης και μετάλλαξης και η ανάλυση των γενετικών παραγόντων που οδηγούν στην ανάπτυξη της εξέλιξης των αισθητήρων (EGP). Με αυτό τον τρόπο υποστηρίζεται η βελτιωμένη απόδοση και η αποτελεσματικότητα των ασαφών γενετικών αλγορίθμων.

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