文件名称:pso_Methods_for_Pattern_Recognition_and_Image_Proc
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- 人工智能/神经网络/遗传算法
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- [PDF]
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- 2012-11-26
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- 5.67mb
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A dissipative particle swarm optimization is
developed according to the self-organization of dissipative
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.-A dissipative particle swarm optimization isdeveloped according to the self-organization of dissipativestructure. The negative entropy is introduced to construct anopening dissipative system that is far-from-equilibrium so as todriving the irreversible evolution process with better fitness.The testing of two multimodal functions indicates it improvesthe performance effectively.structure. The negative entropy is introduced to construct anopening dissipative system that is far-from-equilibrium so as todriving the irreversible evolution process with better fitness.The testing of two multimodal functions indicates it improvesthe performance effectively.
developed according to the self-organization of dissipative
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.
structure. The negative entropy is introduced to construct an
opening dissipative system that is far-from-equilibrium so as to
driving the irreversible evolution process with better fitness.
The testing of two multimodal functions indicates it improves
the performance effectively.-A dissipative particle swarm optimization isdeveloped according to the self-organization of dissipativestructure. The negative entropy is introduced to construct anopening dissipative system that is far-from-equilibrium so as todriving the irreversible evolution process with better fitness.The testing of two multimodal functions indicates it improvesthe performance effectively.structure. The negative entropy is introduced to construct anopening dissipative system that is far-from-equilibrium so as todriving the irreversible evolution process with better fitness.The testing of two multimodal functions indicates it improvesthe performance effectively.
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下载文件列表
Particle Swarm Optimization Methods for Pattern Recognition and Image Processing
................................................................................\00front.pdf
................................................................................\01chapter1.pdf
................................................................................\03chapter3.pdf
................................................................................\04chapter4.pdf
................................................................................\05chapter5.pdf
................................................................................\06chapter6.pdf
................................................................................\07chapter7.pdf
................................................................................\08chapter8.pdf
................................................................................\09back.pdf
................................................................................\00front.pdf
................................................................................\01chapter1.pdf
................................................................................\03chapter3.pdf
................................................................................\04chapter4.pdf
................................................................................\05chapter5.pdf
................................................................................\06chapter6.pdf
................................................................................\07chapter7.pdf
................................................................................\08chapter8.pdf
................................................................................\09back.pdf