scholarly journals ANFIS modeling and sensitivity analysis for estimating solar still productivity using measured operational and meteorological parameters

2017 ◽  
Vol 18 (4) ◽  
pp. 1437-1448 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Sensitivity Analysis ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Solar Still ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Input Parameters ◽  
Statistical Criteria

Abstract This study investigates a potential application of the adaptive neuro-fuzzy inference system (ANFIS) as a relatively new approach for predicting solar still productivity (SSP). Five variables, relative humidity (RH), solar radiation (SR), feed flow rate (MF), and total dissolved solids of feed (TDSF) and brine (TDSB), were used as input parameters. The data were collected from an experimental solar still system used to desalinate seawater in an arid climate. The data were distributed randomly into training, testing, and validation datasets. A hybrid learning algorithm and eight different membership functions were applied to generate the ANFIS models. Several statistical criteria were used to assess the model performances. The ANFIS model with a generalized bell membership function provided the best prediction accuracy compared with models with other membership functions. The coefficient of correlation values for this model were 0.999, 0.959, and 0.832 for training, testing, and validation datasets, respectively. Sensitivity analysis (SA) was used to show the effectiveness of the considered input parameters for predicting SSP. The SA results indicated that SSP is the most influential parameter on SSP. Generally, the findings indicate the robustness of the ANFIS approach for estimating SSP.

Position and Tilt Control of Two-Wheeled Robot (TWR)

2017 ◽  
Vol 6 (4) ◽  
pp. 17-33 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Fuzzy Inference ◽  
Second Law ◽  
Membership Functions ◽  
Inference System ◽  
Control Approach ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Wheeled Robot ◽  
Simulation Results ◽  
Training Error

This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot (TWR) system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference system(ANFIS) controller. A Matlab Simulink model of the system has been built using Newton's second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.

An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

2012 ◽  
Vol 1 (2) ◽  
pp. 44-59 ◽  
Author(s):  
M. S. Abdel Aziz ◽  
M. A. Moustafa Hassan ◽  
E. A. El-Zahab
Keyword(s):  
Artificial Intelligence ◽  
Distribution System ◽  
Fault Location ◽  
Fuzzy Inference ◽  
Distribution Networks ◽  
Inference System ◽  
New Approach ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Faults

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system

2003 ◽  
Vol 32 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Dursun Kaya ◽  
A. Samet Hasiloglu ◽  
Mahmut Bayramoglu ◽  
Hakki Yesilyurt ◽  
A. Fahri Ozok
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anthropometric Measurements ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy

HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 22 (06) ◽  
pp. 1250028 ◽  
Author(s):  
K. SUBRAMANIAN ◽  
S. SURESH
Keyword(s):  
Action Recognition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Sequential Learning ◽  
Inference System ◽  
Neuro Fuzzy

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

scholarly journals Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

2016 ◽  
Vol 28 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Dejan Mirčetić ◽  
Nebojša Ralević ◽  
Svetlana Nikoličić ◽  
Marinko Maslarić ◽  
Đurđica Stojanović
Keyword(s):  
Expert System ◽  
Customer Service ◽  
Fuzzy Inference ◽  
Service Level ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Customer Service Level ◽  
Distribution Chain

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.

scholarly journals PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2013 ◽  
Vol 2 (3) ◽  
pp. 46
Author(s):  
SLAMET SAMSUL HIDAYAT ◽  
I PUTU EKA NILA KENCANA ◽  
KETUT JAYANEGARA
Keyword(s):  
Least Squares ◽  
Public Transportation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Inference System ◽  
Transportation Services ◽  
Neuro Fuzzy ◽  
Two Phases

Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE) on forwards phases and Error Backpropagation (EBP) on the backward phases. The results show, ANFIS with six inputs with M.F of  Pi  produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.

Sign in / Sign up

Export Citation Format

Share Document