according to this fact that wind is now a part of global energy portfolio and due to unreliable and discontinuous production of wind energy; prediction of wind power value is proposed as a main necessity. in recent years, various methods have been proposed for wind power prediction. in this paper the prediction structure involves feature selection and use of artificial neural network (ann). in ...

We benchmark the dynamical simplex evolution (DSE) method with several of the currently available algorithms to detect communities in complex networks by comparing correctly identified nodes for different levels of "fuzziness" of random networks composed of well-defined communities. The potential benefits of the DSE method to detect hierarchical substructures in complex networks are discussed.

We consider the minimum cost network flow problem min(cx : Ax=b , x > 0) on a graph G = (V,E). First we give a minor modification of Edmonds-Karp scaling technique, and we show that it solves the minimum cost flow problem in 0((|V|'^ log 1v|)(|e| + |V| log |v|)) steps. We also provide two dual simplex algorithms that solve the minimum cost flow problem in 0(|v| log |v|) pivots and 0(|v| log |v|...

This paper deals with linear and integer programming problems in which the constraint matrix is a binet matrix. Binet matrices are pivoted versions of the node-edge incidence matrices of bidirected graphs. It is shown that efficient methods are available to solve such optimization problems. Linear programs can be solved with the generalized network simplex method, while integer programs are con...

Transportation problem is a linear programming which considers minimum cost for shipping a product from some origins to other destinations such as from factories to warehouse, or from a warehouse to supermarkets. To solve this problem simplex algorithmis utilized. In real projects costs and the value of supply and demands are fuzzy numbers and it is expected that optimal solutions for determini...

For a robot trying to reach places in at least partially unknown environments there is often a need to replan paths online based on information extracted from the surroundings. In this paper is is assumed that the sensing range of the robot is short compared to the length of the paths it plans and the environment is modeled as a graph consisting of nodes and arcs. The replanning problem is solv...

Dynamic Bayesian networks (DBN) are handy tools to model complex dynamical systems learned from collected data and expert knowledge. However, expert knowledge may be incomplete, and data may be scarce (this is typically the case in Life Sciences). In such cases, using precise parameters to describe the network does not faithfully account for our lack of information. This is why we propose, in t...