Results 291 to 300 of about 2,166,508 (336)
Some of the next articles are maybe not open access.
2012
The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance.
openaire +1 more source
The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance.
openaire +1 more source
Hybrid Difference Methods for PDEs
Journal of Scientific Computing, 2014The author introduces a numerical method for partial differential equations (PDEs) which consists of two steps: the finite difference approximation of PDEs within cells and the interface finite difference on edges of cells. As the author explains in the paper, the method is suitable to non-uniform grids, retaining the optimal order of convergence. Some
openaire +1 more source
1998
To solve the boundary value problems of elliptic equations, especially with singularities and unbounded domains, the simplified hybrid combinations are presented in this chapter, which also adopt the solution flux on the side ∂S2 of S2 where the Ritz-Galerkin method applies.
openaire +1 more source
To solve the boundary value problems of elliptic equations, especially with singularities and unbounded domains, the simplified hybrid combinations are presented in this chapter, which also adopt the solution flux on the side ∂S2 of S2 where the Ritz-Galerkin method applies.
openaire +1 more source
2002
In Chapters 4 and 5 the connectionist, multi-layer architectures of fuzzy systems, called fuzzy inference neural networks, were presented. These architectures are similar to neural networks (see Section 3.1), so learning algorithms can be proposed to tune the parameters of the networks, analo gously to tuning weights in neural networks.
openaire +1 more source
In Chapters 4 and 5 the connectionist, multi-layer architectures of fuzzy systems, called fuzzy inference neural networks, were presented. These architectures are similar to neural networks (see Section 3.1), so learning algorithms can be proposed to tune the parameters of the networks, analo gously to tuning weights in neural networks.
openaire +1 more source
2012
The chapter focuses upon the use of hybrid planning systems that are primarily behavioral in nature. Three approaches are discussed. In the first approach, the authors solve the problem of path planning using a combination of A* algorithm and Fuzzy Inference System. The A* algorithm does the higher-level planning by working on a lower detail map.
openaire +1 more source
The chapter focuses upon the use of hybrid planning systems that are primarily behavioral in nature. Three approaches are discussed. In the first approach, the authors solve the problem of path planning using a combination of A* algorithm and Fuzzy Inference System. The A* algorithm does the higher-level planning by working on a lower detail map.
openaire +1 more source