[8] viXra:1309.0149 [pdf] replaced on 2013-09-22 15:16:09
Authors: Piotr Beling
Comments: 10 Pages.
This paper presents an analysis of complexity of a bridge double dummy problem. Values of both, a state-space (search-space) complexity and a game tree complexity have been estimated.
Category: Artificial Intelligence
[7] viXra:1309.0130 [pdf] submitted on 2013-09-17 16:04:54
Authors: Xiao Hu
Comments: 9 Pages.
We summarize the Storkey Learning Rules for the Hopfield Model, and evaluate performance relative to other learning rules. Hopfield Models are normally used for auto-association, and Storkey Learning Rules have been found to have good balance between local learning and capacity. In this paper we outline different learning rules and summarise capacity results. Hopfield networks are related to Boltzmann Machines: they are the same as fully visible Boltzmann Machines in the zero temperature limit. Perhaps renewed interest in Boltzmann machines will produce renewed interest in Hopfield learning rules?
Category: Artificial Intelligence
[6] viXra:1309.0129 [pdf] submitted on 2013-09-17 18:52:12
Authors: Mohammad Reza Faraji, Xiaojun Qi
Comments: 4 Pages.
Face recognition (FR) is a challenging task in biometrics due
to various illuminations, poses, and possible noises. In this
paper, we propose to apply a novel neutrosophic set (NS)-
based preprocesssing method to simultaneously remove noise
and enhance facial features in original face images.
Category: Artificial Intelligence
[5] viXra:1309.0128 [pdf] submitted on 2013-09-17 19:01:02
Authors: Pinaki Majumdar, S. K. Samanta
Comments: 13 Pages.
In this paper we have introduced the notion of distance between two single valued
neutrosophic sets and studied its properties. We have also defined several similarity measures between
them and investigated their characteristics. A measure of entropy of a single valued neutrosophic set has
also been introduced.
Category: Artificial Intelligence
[4] viXra:1309.0102 [pdf] submitted on 2013-09-16 09:11:51
Authors: Evgeniy Grechnikov, Ricardo Vieira Godoy
Comments: 9 Pages.
In this paper, we study the linear distributed asymptotic consensus problem for a network of dynamic agents whose communication network is modeled by a randomly switching graph. A finite state Markov process dominates each topology corresponding to a state of the process. We address both the cases where the dynamics of the agents is expressed in continuous and discrete time. As long as the consensus matrices are doubly stochastic, convergence to average consensus can be shown to be achieved in the mean square and almost sure sense. A necessary and sufficient condition is the graph resulted from the union of graphs corresponding to the states of the Markov process contains a spanning tree.
Category: Artificial Intelligence
[3] viXra:1309.0071 [pdf] submitted on 2013-09-10 20:24:28
Authors: Said Broumi, Florentin Smarandache, Mamoni Dhar
Comments: 8 Pages.
In this paper we study fuzzy soft matrix based on reference function.Firstly, we define some new operations such as fuzzy soft complement matrix and trace of fuzzy soft matrix based on reference function.Then, we introduced some related properties, and some examples are given. Lastly, we define a new fuzzy soft matrix decision method based on reference function.
Category: Artificial Intelligence
[2] viXra:1309.0030 [pdf] submitted on 2013-09-07 11:50:47
Authors: Pabitra Kumar Maji
Comments: 12 Pages.
In this paper we study the concept of neutrosophic set of
Smarandache. We have introduced this concept in soft sets and de¯ned
neutrosophic soft set. Some de¯nitions and operations have been intro-
duced on neutrosophic soft set. Some properties of this concept have been
established.
Category: Artificial Intelligence
[1] viXra:1309.0029 [pdf] submitted on 2013-09-07 11:52:42
Authors: Pabitra Kumar Maji
Comments: 7 Pages.
The decision making problems in an imprecise environment
has found paramount importance in recent years. Here we consider an
object recognition problem in an imprecise environment. The recognition
strategy is based on multiobserver input parameter data set.
Category: Artificial Intelligence