The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a defuzzification mechanism that is ‘differentiable’ with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system
Diesel engines have become a common source of power, both for vehicles and for static equipment beca...
Effective fault detection, estimation, and isolation are essential for the safety and reliability of...
In recent years, learning algorithms have been proved to learn abstract hierarchical pattern in the ...
Abstract—The paper presents a new system identification methodology for industrial systems. Using th...
The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that...
In the paper, neuro-fuzzy systems (NFSs) for gas turbine diagnostics are studied and developed. The ...
In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The ...
Gas turbines are usually the core elements of numerous mechanical systems. Subsequent to the advance...
Abstract: The paper focuses on the application of neuro-fuzzy techniques in fault detection and isol...
This paper describes the use of a new kind of fuzzy logic system namely, a Takagi-Sugeno-Kang (TSK)-...
In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbin...
The objective of this paper is to estimate the compressor discharge temperature measurements on an i...
A fuzzy system is developed using a linearized performance model of the gas turbine engine for perfo...
In this paper, it is shown that the use of probability‐statistic methods, especially at the early st...
Abstract— Almost from the inception of the gas turbine engine (GT), users and engine manufacturers h...
Diesel engines have become a common source of power, both for vehicles and for static equipment beca...
Effective fault detection, estimation, and isolation are essential for the safety and reliability of...
In recent years, learning algorithms have been proved to learn abstract hierarchical pattern in the ...
Abstract—The paper presents a new system identification methodology for industrial systems. Using th...
The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that...
In the paper, neuro-fuzzy systems (NFSs) for gas turbine diagnostics are studied and developed. The ...
In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The ...
Gas turbines are usually the core elements of numerous mechanical systems. Subsequent to the advance...
Abstract: The paper focuses on the application of neuro-fuzzy techniques in fault detection and isol...
This paper describes the use of a new kind of fuzzy logic system namely, a Takagi-Sugeno-Kang (TSK)-...
In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbin...
The objective of this paper is to estimate the compressor discharge temperature measurements on an i...
A fuzzy system is developed using a linearized performance model of the gas turbine engine for perfo...
In this paper, it is shown that the use of probability‐statistic methods, especially at the early st...
Abstract— Almost from the inception of the gas turbine engine (GT), users and engine manufacturers h...
Diesel engines have become a common source of power, both for vehicles and for static equipment beca...
Effective fault detection, estimation, and isolation are essential for the safety and reliability of...
In recent years, learning algorithms have been proved to learn abstract hierarchical pattern in the ...