A Fuzzy Logic Modeling of Measures Addressing Shipping CO2 Emissions

2017 ◽  
Vol 26 (3) ◽  
pp. 439-455 ◽  
Author(s):  
Evaggelia Lema ◽  
Anastasios Karaganis ◽  
Elpiniki Papageorgiou
Keyword(s):  
Fuzzy Logic ◽  
Domain Knowledge ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Decision Support Tool ◽  
Expert Group ◽  
Inference System ◽  
Support Tool ◽  
Logic Modeling ◽  
Logic System

AbstractThis study presents a decision support tool that uses a fuzzy logic model of expert knowledge to assist in the decision-making process in the context of mitigating shipping CO2 emissions. The issue of selecting a market-based shipping CO2 emission mechanism, as they were presented by the International Maritime Organization (IMO), has generated much discussion over the last decade due to the complexity of the industry. We built a fuzzy logic system using the input and output variables as well as their range values that have been set from the IMO Expert Group study. We developed 27 fuzzy rules based on the literature and our domain knowledge, and we ran the fuzzy inference system for four of the proposed market-based measures. Finally, we evaluated our results and compared them with the IMO Expert Group study. Although not all measures responded the same, especially regarding the emission reductions, the percentage agreement is satisfactory in most of the cases (60–95%). The strength of the tool is that it synthesizes a large amount of information in a logical and transparent framework, and has the potential to have wider application in the context of market-based measures.

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.

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

scholarly journals Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Amir Heydarinasab ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Ant Colony Optimization ◽  
Swarm Intelligence ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Ant Colony ◽  
Output Data ◽  
Inference System ◽  
Learning Framework ◽  
Logic System

AbstractIn the current research paper a novel hybrid model combining first-principle and artificial intelligence (AI) was developed for simulation of a chemical reactor. We study a 2-dimensional reactor with heating sources inside it by using computational fluid dynamics (CFD). The type of considered reactor is bubble column reactor (BCR) in which a two-phase system is created. Results from CFD were analyzed in two different stages. The first stage, which is the learning stage, takes advantage of the swarm intelligence of the ant colony. The second stage results from the first stage, and in this stage, the predictions are according to the previous stage. This stage is related to the fuzzy logic system, and the ant colony optimization learning framework is build-up this part of the model. Ants movements or swarm intelligence of ants lead to the optimization of physical, chemical, or any kind of processes in nature. From point to point optimization, we can access a kind of group optimization, meaning that a group of data is studied and optimized. In the current study, the swarm intelligence of ants was used to learn the data from CFD in different parts of the BCR. The learning was also used to map the input and output data and find out the complex connection between the parameters. The results from mapping the input and output data show the full learning framework. By using the AI framework, the learning process was transferred into the fuzzy logic process through membership function specifications; therefore, the fuzzy logic system could predict a group of data. The results from the swarm intelligence of ants and fuzzy logic suitably adapt to CFD results. Also, the ant colony optimization fuzzy inference system (ACOFIS) model is employed to predict the temperature distribution in the reactor based on the CFD results. The results indicated that instead of solving Navier–Stokes equations and complex solving procedures, the swarm intelligence could be used to predict a process. For better comparisons and assessment of the ACOFIS model, this model is compared with the genetic algorithm fuzzy inference system (GAFIS) and Particle swarm optimization fuzzy inference system (PSOFIS) method with regards to model accuracy, pattern recognition, and prediction capability. All models are at a similar level of accuracy and prediction ability, and the prediction time for all models is less than one second. The results show that the model’s accuracy with low computational learning time can be achieved with the high number of CIR (0.5) when the number of inputs ≥ 4. However, this finding is vice versa, when the number of inputs < 4. In this case, the CIR number should be 0.2 to achieve the best accuracy of the model. This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.

scholarly journals Rain prediction using fuzzy rule based system in North-West malaysia

2019 ◽  
Vol 14 (3) ◽  
pp. 1564
Author(s):  
Noor Zuraidin Mohd Safar ◽  
Azizul Azhar Ramli ◽  
Hirulnizam Mahdin ◽  
David Ndzi ◽  
Ku Muhammad Naim Ku Khalif
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Complex Problem ◽  
Dew Point ◽  
Inference System ◽  
North West ◽  
Rainfall Prediction

<span>The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed.   Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%</span>

Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

2014 ◽  
Vol 984-985 ◽  
pp. 425-430 ◽  
Author(s):  
Thangavel Ramya ◽  
A.C. Kannan ◽  
R.S. Balasenthil ◽  
B. Anusuya Bagirathi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Decision Making Process ◽  
Computing Environment ◽  
Output Variable ◽  
Inference System ◽  
Logic Modeling ◽  
Decision Making Processes

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

scholarly journals Algorithm Apriori Association Rule in Determination of Fuzzy Rule Based on Comparison of Fuzzy Inference System (FIS) Mamdani Method and Sugeno Method

2016 ◽  
Vol 7 (1) ◽  
pp. 103
Author(s):  
Muhammad Fadli Arif ◽  
Bima Anoraga ◽  
Samingun Handoyo ◽  
Harisaweni Nasir
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Percentage Error ◽  
Inference System ◽  
Rule Based ◽  
Accuracy Measurement ◽  
First Order

<p>The economic stability of a country can be determined from the changes in the rate of inflation. Inflation is measured by the annual percentage change in the Consumer Price Index. Since there exists some uncertainties in the inflation data, fuzzy logic is one of the ways to analyse the data. Decisions in fuzzy logic can be made using the fuzzy rule-based inference system. Fuzzy rule-based inference can be obtained from expert knowledge, but the knowledge from the experts on the working of a system is not always available. Therefore, the use of association rules<em> </em>approach could solve the problem. Using three methods of fuzzy inferences; namely the Mamdani Methods, zero-order Sugeno method, and the first-order Sugeno method, this study was carried out to determine which method fits to predict the general monthly inflation data in Indonesia. The Inflation data were derived from the inflation of foodstuff price, <em>X<sub>1</sub></em>; inflation of food, drinks, cigarettes and tobacco prices, <em>X<sub>2</sub></em>; inflation of housing, water, electricity, gas, and fuel prices, <em>X<sub>3</sub></em>; inflation of clothing price, <em>X<sub>4</sub></em>; inflation of health care price, <em>X<sub>5</sub></em>; inflation of education, recreation, and sports prices, <em>X<sub>6</sub></em>; and inflation of transportation, communication, and financial services prices, <em>X<sub>7</sub></em>. The performance of the three methods was compared using mean squared error (MSE) and mean absolute percentage error (MAPE) as the accuracy measurement to establish the best fuzzy inference method that fits the inflation value. It was found that the most appropriate method which generated the most accurate results to fit the fuzzy inference system to the inflation data was the first-order Sugeno method.</p>

scholarly journals Fuzzy Logic in Genetic Regulatory Network Models

2009 ◽  
Vol 4 (4) ◽  
pp. 363 ◽  
Author(s):  
Carlos Muñoz Poblete ◽  
Francisco Vargas Parra ◽  
Jaime Bustos Gomez ◽  
Millaray Curilem Saldias ◽  
Sonia Salvo Garrido ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Differential Equations ◽  
Regulatory Networks ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Genetic Regulatory Networks ◽  
Training Data ◽  
Lac Operon ◽  
Inference System ◽  
Linear Differential

<p>Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). Several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (ODEs). Some approximations using piecewise linear differential equations (PLDEs), have been proposed to simplify the model non linearities. However they not allways give good results. In this context, it has been developed a model capable of representing small GRN, combining characteristics from the ODE’s models and fuzzy inference systems (FIS). The FIS is trained through an artificial neural network, which forms an Adaptive Nertwork-based Fuzzy Inference System (ANFIS). This network allows to adapt the membership and output functions from the FIS according to the training data, thus, reducing the previous knowledge needed to model the specific phenomenon.<br /> In addition, Fuzzy Logic allows to express their rules through linguistic labels, which also allows to incorporate expert knowledge in a friendly way. The proposed model has been used to describe the Lac Operon in E. Coli and it has been compared with the models already mentioned. The outcome errors due to the training process of the ANFIS network are comparable with those of the models based on ODEs. Additionally, the fuzzy logic approach provides modeling flexibility and knowledge acquisition advantages.</p>

scholarly journals EVALUATION OF HOUSING CONSTRUCTION STRATEGIES IN CHINA USING FUZZY‐LOGIC SYSTEM

2005 ◽  
Vol 9 (4) ◽  
pp. 215-232 ◽  
Author(s):  
Yeng-Horng Perng ◽  
Sung-Lin Hsueh ◽  
Min-Ren Yan
Keyword(s):  
Fuzzy Logic ◽  
Housing Market ◽  
Model Building ◽  
Expert Knowledge ◽  
Logic Gate ◽  
Decision Support Tool ◽  
Sales Performance ◽  
Foreign Investors ◽  
Support Tool ◽  
Chinese Housing Market

There is a large demand for and a large profit potential in the Chinese housing market. However, foreign investors still suffer high risk because they are unfamiliar with the complexities of this enormous market. This paper presents the issues associated with housing sales in the People's Republic of China and the applications of Fuzzy Logic and Delphi Method to the prediction of sales performance in China's housing. Furthermore, the study presents the concept of an AND/OR logic gate based model, so that expert knowledge can be converted through computation into the needed rules for decision making related to housing sales performance prediction. The logic gate model is useful for ‘if‐then’ rule analysis, monitoring during the model building and amendment process, and in model maintenance in the later stages of its application. The aim of the described model is to provide a computer‐based decision support tool for foreign investors and contractors in evaluating beforehand the business feasibility of entering the Chinese housing market.

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.

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