Fuzzy Logic in Genetic Regulatory Network Models

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. 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.

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Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Computational Intelligence and Neuroscience ◽

10.1155/2021/8834324 ◽

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.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1564-1573 ◽

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

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%

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

Business Management and Strategy ◽

10.5296/bms.v7i1.9410 ◽

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

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 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, X1; inflation of food, drinks, cigarettes and tobacco prices, X2; inflation of housing, water, electricity, gas, and fuel prices, X3; inflation of clothing price, X4; inflation of health care price, X5; inflation of education, recreation, and sports prices, X6; and inflation of transportation, communication, and financial services prices, X7. 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.

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A Fuzzy Logic Modeling of Measures Addressing Shipping CO2 Emissions

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0161 ◽

2017 ◽

Vol 26 (3) ◽

pp. 439-455 ◽

Cited By ~ 1

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.

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Deflection prediction of cantilever concrete beam using adaptive fuzzy inference system

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.2.12746 ◽

2018 ◽

Vol 7 (2.2) ◽

pp. 108

Author(s):

Sujiati Jepriani ◽

Ibayasid . ◽

Aji Prasetya Wibawa ◽

Leonel Hernandez

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

Concrete Beam ◽

Fuzzy Inference ◽

Initial Position ◽

Training Data ◽

Inference System ◽

Internal Forces ◽

The Moment ◽

External Forces

The cantilever concrete beam is a beam made of concrete that is only supported or clamped at one end and the other end free or without the pedestal. The measure to which a structural member gets deviated from the initial position is called deflection. All the internal forces of cantilever beam serve to hold all the external forces due to the load so that the structure remains balanced. While the beam gets deflected under the loads, bending occurs in the same plane due to which stresses are developed. From the moment balance formula after integrated and solved with required boundary conditions, we get the downward deflection of beam. Fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. FIS (Fuzzy Inference System) is a system that processes the mapping formulation of a given input to produce an output using Fuzzy Logic. By using randomized data for all the variables of deflection formula within their respective ranges as training data, the FIS will be able to predict deflection of cantilever concrete beam after going through the training process adaptively.

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Robot navigation with obstacle avoidance in unknown environment

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.14767 ◽

2018 ◽

Vol 7 (4) ◽

pp. 2410 ◽

Cited By ~ 1

Author(s):

Neerendra Kumar ◽

Zoltán Vámossy

Keyword(s):

Fuzzy Logic ◽

Obstacle Avoidance ◽

Fuzzy Inference System ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Fuzzy Controller ◽

Robot Navigation ◽

Training Data ◽

Inference System ◽

Pure Pursuit

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

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Design of a Fuzzy Logic Controller Using ANFIS for Accurate Position Control of a Pneumatic Servo System

Volume 5: Design, Analysis, Control and Diagnosis of Fluid Power Systems ◽

10.1115/imece2008-66940 ◽

2008 ◽

Author(s):

S. S. Dhami ◽

S. S. Bhasin ◽

P. B. Mahapatra

Keyword(s):

Fuzzy Logic ◽

Fuzzy Inference System ◽

Fuzzy Logic Controller ◽

Position Control ◽

Fuzzy Inference ◽

Servo System ◽

Training Data ◽

Data Set ◽

Inference System ◽

Pneumatic Servo System

A methodology for designing a Sugeno type Fuzzy Logic Controller (FLC) for accurate position control of a pneumatic servo system is presented. Adaptive Neuro Fuzzy Inference System technique is employed to construct a fuzzy inference system whose membership function parameters are tuned using a training data set comprising of input/output signal of the pneumatic servo system with proportional control. Hybrid backpropogation-least square algorithm is used for training of the Fuzzy Inference System (FIS). The resulting FIS optimally projected the behavior of training data set. To obtain the desired steady-state response, the fuzzy inference system is further tuned using the expert knowledge of the input/output response of the system. The system response for various reference inputs is compared quantitatively with that of the system without fuzzy logic controller, and excellent improvement in steady-state response is observed.

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9010049 ◽

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.

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

Advances in Fuzzy Systems ◽

10.1155/2013/343174 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

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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