To overcome this problem, the weights of the networks are trained on line. In this paper, an on-line training algorithm with a computation time that is linear ...
A computation-efficient on-line training algorithm for neurofuzzy ...
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Neurofuzzy networks are often used to model linear or nonlinear processes, as they can provide some insights into the underlying processes and can be ...
In this paper, an on-line training algorithm is proposed that takes full advantage of the local change property of the networks. As the computation time is ...
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A computation-efficient on-line training algorithm for neurofuzzy networks. C. W. Chan K. C. Cheung W. K. Yeung. Published in: Int. J. Syst. Sci. (2000).
The proposed linearization technique is carried out by a very efficiently trained neuro-fuzzy hybrid network which compensates for the sensor's nonlinear ...
An ideal linear sensor is one for which input and output values are always proportional. Typical sensors are, in general, highly nonlinear or seldom ...
The paper describes an algorithm that can be used to train the Takagi-Sugeno (TS) type neuro-fuzzy network very efficiently. The training algorithm is very ...
Sep 30, 2020 · Abstract:General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification.
A computation-efficient on-line training algorithm for neurofuzzy networks. Article. Mar 2000. C.W. Chan · K.C. Cheung · W.K. Yeung. Neurofuzzy networks ...
An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: ...