文件名称:04470122.rar
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- 行业发展研究
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- 2013-02-20
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- Mohd E******
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This paper proposes a novel and computationally efficient global I< optimization method based on swarm ntelligence for locating ti nodes in a WSN environment. The mean squared range error of a
all neighbouring anchor nodes is taken as the objective function d for this non linear optimization problem. The Particle Swarm c
Optimization (PSO) is a high performance stochastic global b optimization tool that ensures the minimization of the objective a
function, ,This paper proposes a novel and computationally efficient global I< optimization method based on swarm ntelligence for locating ti nodes in a WSN environment. The mean squared range error of a
all neighbouring anchor nodes is taken as the objective function d for this non linear optimization problem. The Particle Swarm c
Optimization (PSO) is a high performance stochastic global b optimization tool that ensures the minimization of the objective a
function,
all neighbouring anchor nodes is taken as the objective function d for this non linear optimization problem. The Particle Swarm c
Optimization (PSO) is a high performance stochastic global b optimization tool that ensures the minimization of the objective a
function, ,This paper proposes a novel and computationally efficient global I< optimization method based on swarm ntelligence for locating ti nodes in a WSN environment. The mean squared range error of a
all neighbouring anchor nodes is taken as the objective function d for this non linear optimization problem. The Particle Swarm c
Optimization (PSO) is a high performance stochastic global b optimization tool that ensures the minimization of the objective a
function,
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