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Neural Computing and Applications

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01.12.2013 | ISNN 2012

Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction

verfasst von: Chengdong Li, Jianqiang Yi, Ming Wang, Guiqing Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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This paper studies the monotonic type-2 fuzzy neural network (T2FNN), which can be adopted in many identification and prediction problems where the monotonicity property between the inputs and outputs is required. Sufficient conditions on the parameters of the T2FNN are first presented to ensure the monotonicity between the inputs and outputs. Then, data-driven design model for the monotonic T2FNN is built. Also, under the monotonicity constraints, a hybrid algorithm is provided to optimize the parameters of the monotonic T2FNN. This hybrid algorithm utilizes the constrained least squares method and the penalty function-based gradient descent algorithm to realize reasonable parameter initialization and optimization. At last, an application to the thermal comfort index prediction is given to verify the effectiveness of the monotonic T2FNN. Comparisons with other methods are also made.

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Titel: Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction
verfasst von: Chengdong Li
Jianqiang Yi
Ming Wang
Guiqing Zhang
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1140-x

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