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Recurrent Fuzzy Neural Network (RFNN) Library for Simulink

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Recurrent Fuzzy Neural Network (RFNN) Library for Simulink

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12 Aug 2013 (Updated )

Dynamic, Recurrent Fuzzy Neural Network (RFNN) for on-line Supervised Learning.

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Description

This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy network trained exclusively by error backpropagation at layers 2 and 4. The network employs 4 sets of adjustable parameters. In Layer 2: mean[i,j], sigma[i,j] and Theta[i,j] and in Layer 4: Weights w4[m,j]. The network uses considerably less adjustable parameters than ANFIS/CANFIS and therefore, its training is generally faster. This makes it ideal for on-line learning/operation. Also, its approximating/mapping power is increased due to the employment of dynamic elements within Layer 2. Scatter-type and Grid-type methods are selected for input space partitioning.

[1] C.-H. Lee, C.-C. Teng, Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks, IEEE Transactions on Fuzzy Systems, vol.8, No.4, pp.349-366, Aug. 2000.

Required Products Simulink
MATLAB release MATLAB 7.13 (R2011b)
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Comments and Ratings (2)
17 Apr 2014 Mahamadou Diarra  
19 Nov 2013 Lejla BM  
Updates
23 Sep 2013

Added some details in the Description entru of this form.

24 Sep 2013

Minor corrections in the description of this submission.

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