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Recurrent Fuzzy Neural Network (RFNN) Library for Simulink

4.3 | 3 ratings Rate this file 30 Downloads (last 30 days) File Size: 114 KB File ID: #43021 Version: 1.3
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Recurrent Fuzzy Neural Network (RFNN) Library for Simulink



12 Aug 2013 (Updated )

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

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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.


Adaptive Neuro Fuzzy Inference Systems (Anfis) Library For Simulink inspired this file.

Required Products Simulink
MATLAB release MATLAB 7.13 (R2011b)
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Comments and Ratings (3)
31 Mar 2016 Thanh Liem Dao

17 Apr 2014 Mahamadou Diarra

19 Nov 2013 Lejla BM

23 Sep 2013 1.1

Added some details in the Description entru of this form.

24 Sep 2013 1.2

Minor corrections in the description of this submission.

08 May 2015 1.3

I have killed some redundant variables and commands. The new s-functions are more concise and therefore, easily readable. Naturally, faster execution should come as a result.

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