Recurrent Fuzzy Neural Network (RFNN) Library for Simulink
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.
Zitieren als
Ilias Konsoulas (2024). Recurrent Fuzzy Neural Network (RFNN) Library for Simulink (https://www.mathworks.com/matlabcentral/fileexchange/43021-recurrent-fuzzy-neural-network-rfnn-library-for-simulink), MATLAB Central File Exchange. Abgerufen .
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
Windows macOS LinuxKategorien
- Control Systems > Fuzzy Logic Toolbox >
- AI, Data Science, and Statistics > Deep Learning Toolbox > Function Approximation, Clustering, and Control > Function Approximation and Clustering >
Tags
Quellenangaben
Inspiriert von: Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Library for Simulink
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Live Editor erkunden
Erstellen Sie Skripte mit Code, Ausgabe und formatiertem Text in einem einzigen ausführbaren Dokument.
Demos/Utilities/
S-functions/
Demos/
Library/
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
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. |
|
|
1.2.0.0 | Minor corrections in the description of this submission. |
||
1.1.0.0 | Added some details in the Description entru of this form. |
||
1.0.0.0 |