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.

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 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 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.

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

scholarly journals An Interval Type-2 Adaptive Neuro-Fuzzy Inference System Based, Artificial Pacemaker Design and Stability Analysis

2022 ◽  
Author(s):  
Asghar Dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoun Nooshiravan ◽  
Keivan Maghooli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Square ◽  
Step Response ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Interval Type ◽  
System File

Abstract This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adaptive neuro-fuzzy inference system(ANFIS) pacemaker controller in MATLAB. After designing the type-1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, type-1 (IT1FS) model step response was analyzed and compared with the other PID and Fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions and generation of type-1 fis (fuzzy inference system) file, but at current work Fuzzy C Means (FCM) method that shows better results has been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient with respect to the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.

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.

Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield

2018 ◽  
Vol 29 (1) ◽  
pp. 924-940 ◽  
Author(s):  
Ahmed A. Ewees ◽  
Mohamed Abd Elaziz
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Square ◽  
Support Vector ◽  
Local Optimum ◽  
Gray Wolf ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Whale Optimization ◽  
Benchmark Datasets

Abstract This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.

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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