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Conventional neuro-fuzzy systems cannot effectively cope with dynamic processes, such as the heating systems of the buildings, due to the feed forward network structure. To overcome this problem, the existing hybrid system is incorporated with a feedback loop so that it can model the dynamical behavior of the process. As a case study, this improved hybrid system is employed to build an inferential...
As the neuro-fuzzy system is based on a feed forward network structure, it cannot effectively cope with dynamic processes such as space heating systems for the built environment. To overcome this problem, an improved version of the hybrid system is developed and presented in this paper. This system has self-feedback loops for the output that can model the dynamical behavior of the process. The developed...
Inferential sensors are used to infer the critical control variables that are otherwise difficult, if not impossible, to measure in broad range of engineering fields. All inferential sensors are based on an inferential modelling module that represents the dynamics between the inputs and the outputs. Two commonly used artificial intelligence based approaches for the development of the inferential modelling...
The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage...
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