Parameter selection for training process of neuro-fuzzy systems for average air temperature estimation

Jassar, S.; Zhao, Lian; Liao, Zaiyi; Ng, Ka Long Ringo

doi:10.1109/icma.2009.5246537

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Good agreement can be observed. This indicates that the model used for estimation is correctly structured and can effectively estimate the building air temperature [120].…”

Section: Anfis-submentioning

confidence: 87%

Grey box modelling and advanced control scheme for building heating systems

Jassar¹

2021

Preprint

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This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modelling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design and inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on measurement of the air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.

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“…Good agreement can be observed. This indicates that the model used for estimation is correctly structured and can effectively estimate the building air temperature [120].…”

Section: Anfis-submentioning

confidence: 87%

Grey box modelling and advanced control scheme for building heating systems

Jassar¹

2021

Preprint

Self Cite

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“…Clustering operation can be implemented in two different ways: grid partitioning or subtractive clustering methods. In grid partitioning method, the number of membership functions is adjusted manually by the user via dividing the data set into rectangular subspaces using axis-parallel partition (Jassar et al, 2009). Subtractive clustering method performs automatically membership function prediction regarding each data point to have a potential for being cluster center, and it calculates the likelihood of each data point that would define the cluster center, based on the density of neighborhood data points (Chiu, 1994).…”

Section: Nonlinear Mathematical Model Of the Turboprop Enginementioning

confidence: 99%

Simulation-based steady-state aero-thermal model for small-scale turboprop engine

Yazar¹,

Yasa

Kiyak

2017

AEAT

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Purpose An aircraft engine control system consists of a large scale of control parameters and variables because of the complex structure of aero-engine. Monitoring and adjusting control variables and parameters such as detecting, isolating and reconfiguring the system faults/failures depend on the controller design. Developing a robust controller is based on an accurate mathematical model. Design/methodology/approach In this study, a small-scale turboprop engine is modeled. Simulation is carried out on MATLAB/Simulink for design and off-design operating conditions. Both steady-state and transient conditions (from idle to maximum thrust levels) are tested. The performance parameters of compressor and turbine components are predicted via trained Neuro-Fuzzy model (ANFIS) based on component maps. Temperature, rotational speed, mass flow, pressure and other parameters are generated by using thermodynamic formulas and conservation laws. Considering these calculated values, error calculations are made and compared with the cycle data of the engine at the related simulation conditions. Findings Simulation results show that the designed engine model’s simulation values have acceptable accuracy for both design and off-design conditions from idle to maximum power operating envelope considering cycle data. The designed engine model can be adapted to other types of gas turbine engines. Originality/value Different from other literature studies, in this work, a small-scale turboprop engine is modeled. Furthermore, for performance prediction of compressor and turbine components, ANFIS structure is applied.

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Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

Isin

Uzunsoy

2013

Arab J Sci Eng

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Parameter selection for training process of neuro-fuzzy systems for average air temperature estimation

Cited by 6 publications

References 9 publications

Grey box modelling and advanced control scheme for building heating systems

Grey box modelling and advanced control scheme for building heating systems

Simulation-based steady-state aero-thermal model for small-scale turboprop engine

Predicting the Exhaust Emissions of a Spark Ignition Engine Using Adaptive Neuro-Fuzzy Inference System

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