The inverted pendulum is a highly nonlinear and open loop unstable system. To develop an accurate model of the inverted pendulum, different linear and nonlinear methods of identification will be used. However one of the problems encountered during modeling is the collection of experimental data from the inverted pendulum system. Since the output data from the unstable system does not show enoug...

In this paper, the use of fuzzy models relating rainfall to catchment discharge is investigated for the Zwalm catchment in Belgium. The models are built along the lines of Gaweda’s method [4]. Since acceptable models were not obtained for this data set, the method was further adapted. The newly obtained models are of comparable performance as Takagi–Sugeno models based on the Gustafson–Kessel c...

The banking sector as one of the economic drivers plays an important role in society. Over time, bank operations did not only raise funds from public but were more complex. development industry can be seen number banks Indonesia that have spurred level competition. Of course, must pay attention to its health. use soundness parameters or RGEC combined with clusters is interesting study. By using...

The more sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the EM algorithm for a mixture of Gaussians. However, these additional degrees of freedom often reduce the robustness of the algorithm...

More sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the expectation maximization (EM) algorithm for a mixture of Gaussians. However, these additional degrees of freedom can reduce the robust...

We introduce an objective function-based fuzzy clustering technique that incorporates linear combinations of attributes in the distance function. The main application eld of our method is image processing where a comparison pixel by pixel is usually not adequate, but the environmnet of a pixel or groups of pixels characterize important properties of an image or parts of it. In addition, our app...