Name
Abstract | ||
|---|---|---|
Fuzzy mean square clustering is one of the simplest and most performant versions of the k-means non-hierarchical clustering methods. In this work, we extend and improve this method by a recurrent neural network, leading to a new clustering method called Recurrent Neural Network Fuzzy Mean Square. In this approach the fuzzy mean square error is modeled by a constrained non-linear optimization program. The latter is solved by a recurrent neural network in which an original energy function is defined. The energy function makes a compromise between the objective function and the constraints by using appropriate Lagrange relaxation scales. The Euler-Cauchy method is then used to calculate the centers and the membership functions. Simulation results on academic datasets show the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CloudTech49835.2020.9365873 | 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech) |
Keywords | DocType | ISBN |
component,Fuzzy logic,Recurrent Neural networks,constrained non-linear optimization program | Conference | 978-1-7281-6176-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karim El Moutaouakil | 1 | 0 | 0.34 |
| Abdellah Touhafi | 2 | 121 | 32.13 |