Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share original data with each other and can only communicate non-sensitive information neighbors. However, existing DSSL cope uncertainties may suffer from high computation communication overhead problems. To handle the...

In regression problems, the use of TSK fuzzy systems is widely extended due to the precision of the obtained models. Moreover, the use of simple linear TSK models is a good choice in many real problems due to the easy understanding of the relationship between the output and input variables. In this paper we present FRULER, a new genetic fuzzy system for automatically learning accurate and simpl...

We address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the...

This work deals with the problem of the Stationary Discrete Extremal Fuzzy Dynamic System (SDEFDS) identification and briefly discusses the results developed by G. Sirbiladze [10]–[17]. Applying the results of [10] and [11], the fuzzy process with possibilistic uncertainty, the source of which is expert knowledge reflections, is constructed [12]. The dynamics of SDEFDS is described. Based on th...

Dedicated to Rafael Sorkin, our friend and teacher, and a true and creative seeker of knowledge. Preface One of us (Balachandran) gave a course of lectures on " Fuzzy Physics " during spring, 2002 for students of Syracuse and Brown Universities. The course which used video conferencing technology was also put on the websites [1]. Subsequently A.P. Balachandran, S. Kürkçüoˇglu and S.Vaidya decid...

In this paper we propose a new nonparametric regression algorithm based on Fuzzy systems with overlapping concepts. We analyze its consistency properties, showing that it is capable to reconstruct an infinite-dimensional class of function when the size of the (noisy) dataset grows to infinity. Moreover convergence to the target function is guaranteed in Sobolev norms so ensuring uniform converg...