Function Approximation Capability of a Novel Fuzzy Flip-Flop Based Neural Network
| Title | Function Approximation Capability of a Novel Fuzzy Flip-Flop Based Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2009 |
| Publication Language | English |
| Pagination | 1900-1907 |
| Authors | Lovassy, R., L. T. Kóczy, and L. Gál |
| Conference Name | International Joint Conference on Neural Networks, IJCNN |
| Conference Location | Atlanta, USA |
| Abstract | The function approximation capability of various connectionist systems has been one of the most interesting problems. A method for constructing Multilayer Perceptron Neural Networks (MLP NN) with the aid of fuzzy operations based flip-flops able to approximate single and multiple variable functions is proposed. This paper introduces the concept of fuzzy flip-flop based neural network, particularly by deploying three types of fuzzy flip-flops as neurons. A comparative study of feedbacked fuzzy J-K and two kinds of fuzzy D flip-flops used as neurons, based on fuzzy algebraic, Yager, Dombi, Hamacher and Frank operations is given. Simulation results are presented for several test functions. |