Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Chen, Guanrong; Pham, Trung Tat

doi:10.1201/9781420039818

Cited by 285 publications

(223 citation statements)

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“…This type of systems can model with fuzzy logic approximately. Thus, fuzzy system modelling plays an important role to describe complicated real world systems [5].…”

Section: Fuzzy Setsmentioning

confidence: 99%

Fuzzy Linear Programming Approach for Determining the Production Amounts in Poultry Industry

2016

8th International Conference on Latest Trends in Engineering and Technology (ICLTET-2016) May 5-6 2016 Dubai (UAE)

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Abstract-In recent years, rapid and correct decision making is crucial for both people and enterprises. However, uncertainty makes decision-making difficult. Fuzzy logic is used for coping with this situation. Thus, fuzzy linear programming models are developed in order to handle uncertainty in objective function and the constraints. In this study, a problem of a factory in poultry industry is investigated, required data is obtained and the problem is figured out as a fuzzy linear programming model. The model is solved using Zimmerman approach which is one of the approaches for fuzzy linear programming. As a result, the solution gives the amount of production for each product type in order to gain maximum profit.

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“…This type of systems can model with fuzzy logic approximately. Thus, fuzzy system modelling plays an important role to describe complicated real world systems [5].…”

Section: Fuzzy Setsmentioning

confidence: 99%

Fuzzy Linear Programming Approach for Determining the Production Amounts in Poultry Industry

2016

8th International Conference on Latest Trends in Engineering and Technology (ICLTET-2016) May 5-6 2016 Dubai (UAE)

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“…A finite fuzzy logic implication statement can always be described by a set of general fuzzy IF-THEN rules containing only the fuzzy logic AND operation, in the following multi-input single-output form [8]: The basic structure of the type of fuzzy inference system seen thus far is a model that maps input characteristics to input membership functions, input membership functions to rules, rules to a set of output characteristics, output characteristics to output membership functions, and the output membership function to a single-valued output or a decision associated with the output. [9] The ANFIS is used to implement the proposed model .It uses the collected training data to train the fuzzy detection system which have 4 inputs with one output.…”

Section: B Fuzzy Modelingmentioning

confidence: 99%

Surge Detection System Using Gaussian Curve Membership Function

Waly¹,

Tarrad²,

Fouad³

2014

IJAREEIE

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ABSTRACT:Problem:Surge is an instability that affects the entire compression system. Surge is characterized by a limit cycle oscillation that results in large amplitude fluctuations of the pressure and flow rate. In contrast to rotating stall, during surge the average mass flow is unsteady but circumferentially uniform. Approach: The attempt to find a simple model structure which can capture in some appropriate sense the key of the dynamical properties of the physical plant, we study the application possibilities of the anti-surge detection system by using Gaussian curve membership function to protect the centrifugal compressor from surge by using Neural fuzzy. Results: By testing the new NeuralFuzzy system which has been created for demonstrate the validity of proposed control scheme, the new neural-fuzzy system get a good results compared by the used detection system. Conclusion: We can use Neural-Fuzzy system to detect the surge in centrifugal compressor Alternative to current detection system. KEYWORDS:Compressor, ASC, Gaussian, neural, fuzzy, ANFIS I.INTRODUCTIONCOMPRESSORS are used as part of a gas turbine for jet and marine propulsion or power generation, in superchargers and turbochargers for internal combustion engines, and in a wide variety of industrial processes .In this thesis we focus on centrifugal compressors that are used in (petro) chemical process plants and in fluid transportation pipelines. Centrifugal compressors have the same operating principle as axial compressors so whenever appropriate we will refer to the more general class of turbo compressors that covers all continuous flow compressors. [1] A Compressor transfers kinetic energy from an aero-mechanically-driven rotor to a steady flow of gas. The pressure of the gas is raised by converting the acceleration imparted by the rotating parts of the compressor via diffusion. In normal operation of a compressor, the flow is nominally steady and axisymmetric. The pressure rise is dependent on the speed of rotation, but the efficient range is limited. This instability can take on one of two forms, either surge or rotating stall depending upon the compressor speed. The performance of a compressor is plotted as pressure ratio versus mass flow for different rotational speeds. The plot is divided into two regions by the stall (or surge) line. This line defines the operation limits of the compressor. To the left of the stall line the flow is no longer steady. [2] During normal and slowly changing operations, surge can be avoided by recycling gas through the surge control valve to maintain a minimum flow. However, when a trip or major upset occurs, flow rate drops and the primary means by which surge energy can be reduced to lower the head (suction to discharge pressure difference) at which the compressor reaches the surge (minimum stable) flow condition. The head across a compressor during a trip or upset is dependent on the response of the entire system including changing performance of the compressor, transient flows within the piping, control sy...

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“…Automatic control methods based on artificial intelligence (e.g., Fuzzy Systems, Neural Networks, and Genetic Algorithms) have emerged as an alternative model to analytic control theory [18]. One of the greatest advantages of fuzzy controllers is the simple and intuitive design, which is usually based on the designer experience and experiments.…”

Section: Fuzzy Frequency Phase Lock Loopsmentioning

confidence: 99%

INS Assisted Fuzzy Tracking Loop for GPS-Guided Missiles and Vehicular Applications

Kamel

Renaudin

Nielsen

2013

International Journal of Navigation and Observation

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Autonomous Navigation Systems used in missiles and other high dynamic platforms are mostly dependent on the Global Positioning System (GPS). GPS users face limitations in terms of missile high dynamics and signal interference. Receiver's tracking loops bandwidth requirements to avoid these problems are conflicting. The paper presents a novel signal frequency and phase tracking algorithm for very high dynamic conditions, which mitigates the conflicting choice of bandwidths and reduces tracking loop measurement noise. It exploits the flexibility of fuzzy control systems for directly generating the required Numerically Controlled Oscillator (NCO) tuning frequency using phase and frequency discriminators information and is labeled Fuzzy Frequency Phase Lock Loop (FFPLL). Because Fuzzy Systems can be computationally demanding and an Inertial Navigation System (INS) is often onboard the vehicle, an assisted INS Doppler version has been designed and is also proposed. Assessment of the new GPS tracking method is performed with both simulated and experimental data under jamming conditions. The main enhancements of the proposed system consist in reduced processing time, improved tracking continuity and faster reacquisition time.

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Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Cited by 285 publications

References 0 publications

Fuzzy Linear Programming Approach for Determining the Production Amounts in Poultry Industry

Fuzzy Linear Programming Approach for Determining the Production Amounts in Poultry Industry

Surge Detection System Using Gaussian Curve Membership Function

INS Assisted Fuzzy Tracking Loop for GPS-Guided Missiles and Vehicular Applications

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