scholarly journals River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2427
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Martina Zelenakova ◽  
Mustafa Çalışıcı ◽  
Bestami Taşar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
River Flow ◽  
Fuzzy Inference ◽  
Accurate Determination ◽  
Daily Rainfall ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Inference System ◽  
Flow Estimation ◽  
Generation Technique

Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

scholarly journals Underground Risk Index Assessment and Prediction Using a Simplified Hierarchical Fuzzy Logic Model and Kalman Filter

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 103 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
Do-Hyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Fuzzy Inference ◽  
Risk Index ◽  
Significant Risk ◽  
Assessment Method ◽  
Logic Model ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Logic Model

Normally, most of the accidents that occur in underground facilities are not instantaneous; rather, hazards build up gradually behind the scenes and are invisible due to the inherent structure of these facilities. An efficient inference system is highly desirable to monitor these facilities to avoid such accidents beforehand. A fuzzy inference system is a significant risk assessment method, but there are three critical challenges associated with fuzzy inference-based systems, i.e., rules determination, membership functions (MFs) distribution determination, and rules reduction to deal with the problem of dimensionality. In this paper, a simplified hierarchical fuzzy logic (SHFL) model has been suggested to assess underground risk while addressing the associated challenges. For rule determination, two new rule-designing and determination methods are introduced, namely average rules-based (ARB) and max rules-based (MRB). To determine efficient membership functions (MFs), a module named the heuristic-based membership functions allocation (HBMFA) module has been added to the conventional Mamdani fuzzy logic method. For rule reduction, a hierarchical fuzzy logic model with a distinct configuration has been proposed. In the simplified hierarchical fuzzy logic (SHFL) model, we have also tried to minimize rules as well as the number of levels of the hierarchical structure fuzzy logic model. After risk index assessment, the risk index prediction is carried out using a Kalman filter. The prediction of the risk index is significant because it could help caretakers to take preventive measures in time and prevent underground accidents. The results indicate that the suggested technique is an excellent choice for risk index assessment and prediction.

scholarly journals Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan

Tech-E ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 49
Author(s):  
Sri Redjeki
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Original Data ◽  
Poverty Rate ◽  
Poor People ◽  
Membership Functions ◽  
Test Results ◽  
Inference System ◽  
Poverty Relief ◽  
Correct Data

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results

scholarly journals Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Frumen Olivas ◽  
Ivan Amaya ◽  
José Carlos Ortiz-Bayliss ◽  
Santiago E. Conant-Pablos ◽  
Hugo Terashima-Marín
Keyword(s):  
Fuzzy Logic ◽  
Knapsack Problem ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rules ◽  
Computational Time ◽  
Exact Methods ◽  
Traditional Methods ◽  
Inference System ◽  
Model Complex

Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.

ANFIS for Predicting Surface Roughness in Turning Operation Performed on CNC Lathe

2011 ◽  
Vol 110-116 ◽  
pp. 1793-1798
Author(s):  
M.A. Vinod Kumar
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Membership Functions ◽  
Ic Engine ◽  
Inference System ◽  
Cnc Lathe ◽  
Testing Data ◽  
Inference Systems

For mass production, mainly automation is used, in which cutting parameters are set to obtain required surface roughness. The parts like IC Engine piston, cylinders require very smooth surface finish. The same is the case of sleeves, collets etc., of machine parts. These are made by automatic machining operations. To get approximate value of required surface roughness, the cutting parameters that are to be set with help of Adaptive Neuro Fuzzy Inference System (ANFIS) that is designed by using Fuzzy Logic Toolbox. The Fuzzy Logic Toolbox is a collection of functions built on the MATLAB numeric computing environment. It provides tools to create and edit fuzzy inference systems (FIS) within the framework of MATLAB. ANFIS constructs a relation between given parameters (input data and output data), when it is trained with experimentally predetermined values. It consists of different functions, of which bell and triangular membership functions are used for our purpose. The comparison of accuracy of predicted values for both membership functions are performed using testing data. The training and testing data was obtained performing operation on CNC lathe for 50 work pieces of which 40 were used for training ANFIS and the remaining 10 were used for comparing the accuracy of both Bell and Triangular membership functions. The detailed analysis and procedure is presented.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

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