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本文提出了一种新的基于细菌生存优化(Bacterial Foraging Optimization –BFO)的非线性模型辨识方法。它是利用群集智能仿生BFO算法对一类Hammerstein系统进行辨识，从而估计出它的参数模型-This paper presents a new optimization based on bacterial survival (Bacterial Foraging Optimization-BFO) nonlinear model identification
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原始的细菌群趋药性优化算法，能够解决多维连续函数优化的问题，具有收敛性好，运算速度快等特点-The original group of bacterial chemotaxis optimization algorithm can solve the problem of multi-dimensional continuous function optimization, convergence, high operation speed characteristics
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原始的BCC算法的文章，详细描述了细菌趋药性算法的流程和实现方式-Original article of the BCC algorithm, described in detail the process of bacterial chemotaxis algorithm and implementation
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细菌觅食优化算法，应用方便，算是一种比较新的优化算法-Bacterial foraging optimization algorithm, easy application, regarded as a relatively new optimization algorithm
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细菌群体趋药性算法同时使用单个细菌在引诱剂环境下的应激反应动作和细菌群体间的位置信息交互来进行函数优化.-Bacterial colony chemotaxis algorithm using both information exchange position of the individual bacterial stress response action and the bacterial population in the environment between attractant
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