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An adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts, in a way analogous to either continuous physiological homeostasis or evolutionary adaptation in biology. Feedback loops represent a key feature of adaptive systems, such as ecosystems and individual organisms; or in the human world, communities, organizations, and families.
An adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts, in a way analogous to either continuous physiological homeostasis or evolutionary adaptation in biology. Feedback loops represent a key feature of adaptive systems, such as ecosystems and individual organisms; or in the human world, communities, organizations, and families.
<font color="#ff8000"> 适应系统adaptive system</font>是一组相互作用或相互依存的实体，它们们或真实或抽象，形成一个能够共同响应环境变化或相互作用部分变化的完整的整体。类似于生物学中持续的生理稳态或进化适应。反馈循环代表了适应系统的一个关键特征，例如生态系统和个体有机体; 或者在人类世界、社区、组织和家庭中。
Let <math>S</math> be an arbitrary system subject to changes in time <math>t</math> and let <math>E</math> be an arbitrary event that is a stimulus for the system <math>S</math>: we say that <math>S</math> is an adaptive system if and only if when t tends to infinity <math>(t\rightarrow \infty)</math> the probability that the system <math>S</math> change its behavior <math>(S\rightarrow S')</math> in a time step <math>t_0</math> given the event <math>E</math> is equal to the probability that the system change its behavior independently of the occurrence of the event <math>E</math>. In mathematical terms:
Let <math>S</math> be an arbitrary system subject to changes in time <math>t</math> and let <math>E</math> be an arbitrary event that is a stimulus for the system <math>S</math>: we say that <math>S</math> is an adaptive system if and only if when t tends to infinity <math>(t\rightarrow \infty)</math> the probability that the system <math>S</math> change its behavior <math>(S\rightarrow S')</math> in a time step <math>t_0</math> given the event <math>E</math> is equal to the probability that the system change its behavior independently of the occurrence of the event <math>E</math>. In mathematical terms:
假设s是一个随时间变化的任意系统，t是一个随时间变化的系统，e是一个随时间变化的事件，是系统s的一个刺激: 我们说s是一个适应性系统，当且仅当 t 趋向于无穷时(t→∞)，给定事件E的系统s在时间步长t0改变其行为（S→S′）的概率等于系统改变其行为独立于事件的发生的概率，用数学术语来表达如下：
Thus, for each instant <math>t</math> will exist a temporal interval <math>h</math> such that:
Thus, for each instant <math>t</math> will exist a temporal interval <math>h</math> such that:
因此，对于每一个即时t，都会存在一个时间间隔h，这样:
In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaptation to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications. Physicists have shown that adaptation to the edge of chaos occurs in almost all systems with feedback.
In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaptation to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications. Physicists have shown that adaptation to the edge of chaos occurs in almost all systems with feedback.
在自适应系统中，参数变化缓慢，没有优先值。然而，在一个自调整系统中，参数值“取决于系统动力的历史”。自调节系统最重要的特性之一是它能“适应混沌的边缘”或避免混沌的能力。实际上，如果一个领导者走向混乱的边缘而不走得更远，那么他就可以在没有灾难的情况下自发地行动。2009年3/4月的一篇文章进一步解释了自我调节系统的使用和现实意义。物理学家已经证明，对混沌边缘的适应几乎发生在所有具有反馈的系统中。
How do various types of adaptations interact in a living system? Practopoiesis, a term due to its originator Danko Nikolić, is a reference to a hierarchy of adaptation mechanisms answering this question. The adaptive hierarchy forms a kind of a self-adjusting system in which autopoiesis of the entire organism or a cell occurs through a hierarchy of allopoietic interactions among components. This is possible because the components are organized into a poietic hierarchy: adaptive actions of one component result in creation of another component. The theory proposes that living systems exhibit a hierarchy of a total of four such adaptive poietic operations:
How do various types of adaptations interact in a living system? Practopoiesis, a term due to its originator Danko Nikolić, is a reference to a hierarchy of adaptation mechanisms answering this question. The adaptive hierarchy forms a kind of a self-adjusting system in which autopoiesis of the entire organism or a cell occurs through a hierarchy of allopoietic interactions among components. This is possible because the components are organized into a poietic hierarchy: adaptive actions of one component result in creation of another component. The theory proposes that living systems exhibit a hierarchy of a total of four such adaptive poietic operations:
在一个生命系统中，各种类型的适应性是如何相互作用的？<font color="#ff8000"> 拓扑实践Practopoiesis</font>，这个术语源于它的发明者 Danko nikoli，是对回答这个问题的适应机制层次的一个参考。这种适应性层次结构形成了一种自我调节系统，其中整个生物体或细胞的自创生是通过各组分之间的异体生成相互作用而发生的。这是可能的，因为组件被组织成一个极端层次结构: 一个组件的自适应操作导致另一个组件的创建。该理论提出，生命系统展示了一个由四个这样的适应性极化操作组成的等级体系:
As the hierarchy evolves towards higher levels of organization, the speed of adaptation increases. Evolution is the slowest; the final cell function is the fastest. Ultimately, practopoiesis challenges current neuroscience doctrine by asserting that mental operations primarily occur at the homeostatic, anapoietic level (iii) — i.e., that minds and thought emerge from fast homeostatic mechanisms poietically controlling the cell function. This contrasts the widespread belief that thinking is synonymous with neural activity (i.e., with the 'final cell function' at level iv).
As the hierarchy evolves towards higher levels of organization, the speed of adaptation increases. Evolution is the slowest; the final cell function is the fastest. Ultimately, practopoiesis challenges current neuroscience doctrine by asserting that mental operations primarily occur at the homeostatic, anapoietic level (iii) — i.e., that minds and thought emerge from fast homeostatic mechanisms poietically controlling the cell function. This contrasts the widespread belief that thinking is synonymous with neural activity (i.e., with the 'final cell function' at level iv).
随着等级制度向更高层次的组织发展，适应的速度也在加快。进化是最慢的，最后的细胞功能是最快的。最终，实践拓扑学挑战当前的神经科学学说，认为心理活动主要发生在稳态，非生物水平上。也就是说，头脑和思想从快速的稳态机制中产生，从而控制了细胞功能。这与人们普遍认为的思考是神经活动的同义词(也就是说，与第四级的“最终细胞功能”)形成了鲜明对比。
* [[Adaptive immune system]]
* [[Adaptive immune system]]
适应免疫系统
* [[Artificial neural network]]
* [[Artificial neural network]]
人工神经网络
* [[Complex adaptive system]]
* [[Complex adaptive system]]
复杂适应系统
* [[Diffusion of innovations]]
* [[Diffusion of innovations]]
创新扩散
* [[Ecosystems]]
* [[Ecosystems]]
生态系统
* [[Gaia hypothesis]]
* [[Gaia hypothesis]]
盖亚假说
* [[Gene expression programming]]
* [[Gene expression programming]]
基因表达式编程算法
* [[Genetic algorithms]]
* [[Genetic algorithms]]
基因算法
* [[Learning]]
* [[Learning]]
学习
* [[Neural adaptation]]
* [[Neural adaptation]]
神经适应
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