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Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.
      
In addition, considering the non-convex and non-concave nature of the sub-problem of combinational optimization, the branch-and-bound technique was adopted to obtain or approximate a global optimal solution.
      
To speed up the search process and guarantee a global optimal result, the extended compact genetic algorithm (ECGA) is used to carry out the search process.
      
These schemes are based on a unified theoretical base-sufficient conditions for the global optimal known in optimal control theory.
      
2D and 3D ASMs are combined to obtain a "global optimal" segmentation of the 3D object embedded in the data set, rather than the "locally optimal" segmentation on separate slices.
      
First, the timing-driven placement algorithm is used to find the global optimal solution.
      
In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algorithm has to be adopted.
      
In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assignment algorithm and a 2-stage association algorithm based on a statistic test.
      
A signal subspace based iteration algorithm for sensor location calibration is developed and its convergence to the global optimal point has been shown.
      
The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains.
      
Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values.
      
Every peer in the P2P network is capable of updating its routing table in a real-time way, which enables agents to dynamically and automatically select, according to current traffic condition of the network, the global optimal traversal path.
      
Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained.
      
The global optimal value was attained by repeatedly iterating.
      
The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively.
      
The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm.
      
Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm
      
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm.
      
The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
      
Using insights from the Auxiliary Problem Principle (APP), we show that under both coordination mechanisms the divisional proposals converge to the global optimal solution of (DCA).
      
 

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