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Description: 地球模拟器，最经典的人工生命模型,研究人工智能的必看。一般地说，生命都具有新陈代谢、复制和进化的能力。在自然界中，生物是由有限的食物供给和有限的生存空间约束的。在Tierra中，“生物”由一系列能够自我复制的机器代码或程序组成，它在计算机中的复制分别受到计算机的存储空间和CPU时间约束。能有效地占有内存空间和利用CPU时间的生物体，将具有更高的适应度，传递到下一代的机会就越大。
在Tierra中，计算机的RAM（随机访问存储器）中有一块专门的空间，这个空间中放置了一个“祖先有机体”，该祖先有机体根据它的汇编程序代码中的指令开始复制对它的生存是基本的代码。随着有机体的数目的增加，RAM中的空间减少了，因此有机体为了自己的生存空间开始竞争。-Earth Simulator, the most classic of artificial life model, the study of artificial intelligence Watchable. Generally speaking, life is metabolism, reproduction and the ability of evolution. In nature, biomass is limited by the supply of food and limited living space constraints. In Tierra, "biological" to a series of self-replicating machine code or procedure, in which computers were copied by the computer storage space and CPU time constraints. Effective occupy memory space and CPU time using the organisms, will have a higher degree of adaptability, conveying to the next generation the greater the chances. In Tierra, computer RAM (random access memory) is a specialized space, the space had placed an "ancestral organisms," the ancestors of organisms under its code comp
Platform: |
Size: 269421 |
Author: 陈谭 |
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Description: Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its friendly interface, operation directly. Although not every agent to a virtual computer assembly, but Agent and the environment coupling can be seen as a Turing Machine model, the Agent can mutate and "arbitrary" programmed. Through Autolife model, the first one c
Platform: |
Size: 71637 |
Author: 姚启迪 |
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Description: Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its friendly interface, operation directly. Although not every agent to a virtual computer assembly, but Agent and the environment coupling can be seen as a Turing Machine model, the Agent can mutate and "arbitrary" programmed. Through Autolife model, the first one c
Platform: |
Size: 71680 |
Author: 姚启迪 |
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Description: 地球模拟器，最经典的人工生命模型,研究人工智能的必看。一般地说，生命都具有新陈代谢、复制和进化的能力。在自然界中，生物是由有限的食物供给和有限的生存空间约束的。在Tierra中，“生物”由一系列能够自我复制的机器代码或程序组成，它在计算机中的复制分别受到计算机的存储空间和CPU时间约束。能有效地占有内存空间和利用CPU时间的生物体，将具有更高的适应度，传递到下一代的机会就越大。
在Tierra中，计算机的RAM（随机访问存储器）中有一块专门的空间，这个空间中放置了一个“祖先有机体”，该祖先有机体根据它的汇编程序代码中的指令开始复制对它的生存是基本的代码。随着有机体的数目的增加，RAM中的空间减少了，因此有机体为了自己的生存空间开始竞争。-Earth Simulator, the most classic of artificial life model, the study of artificial intelligence Watchable. Generally speaking, life is metabolism, reproduction and the ability of evolution. In nature, biomass is limited by the supply of food and limited living space constraints. In Tierra, "biological" to a series of self-replicating machine code or procedure, in which computers were copied by the computer storage space and CPU time constraints. Effective occupy memory space and CPU time using the organisms, will have a higher degree of adaptability, conveying to the next generation the greater the chances. In Tierra, computer RAM (random access memory) is a specialized space, the space had placed an "ancestral organisms," the ancestors of organisms under its code comp
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Size: 269312 |
Author: 陈谭 |
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Description: C Programming and microcontrollers are two big topics, practically continental in size, and like continents, are easy to get lost in. Combining the two is a little like traipsing from Alaska to Tierra del Fuego. Chances are you’ll get totally lost and if the natives don’t eat you, your infected blisters will make you want to sit and pout. I’ve been down this road so much that I probably have my own personal rut etched in the metaphorical soil
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Size: 371712 |
Author: Ray |
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Description: Modelo de dos rayos de Andrea Goldsmith (figura 2.5, según expresión
2.12)
f = 0.9 frecuencia en GHz
landa = 0.3/f en m
R = -1 coeficiente de reflexión en tierra
ht = 10 altura del transmisor en m
hr = 2 altura del receptor en m
Gt = 1
Gr = 1
Pt = 1
d=10:.1:100000
phase_diff = 4*pi*ht*hr./(landa*d) aproximación
Pr = Pt*((landa/(4*pi))^2)*((abs((sqrt(Gt)./d) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff)))./d)).^2)
Pr = Pt*((landa/(4*pi))^2)*(1./d).*((abs((sqrt(Gt)) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff))))).^2)
figure(1), clf,
plot((d),10*log10(Pr/max(abs(Pr))))
grid
xlabel( log_1_0(d) )
ylabel( Potencia recibida (dBm) )- Modelo de dos rayos de Andrea Goldsmith (figura 2.5, según expresión
2.12)
f = 0.9 frecuencia en GHz
landa = 0.3/f en m
R = -1 coeficiente de reflexión en tierra
ht = 10 altura del transmisor en m
hr = 2 altura del receptor en m
Gt = 1
Gr = 1
Pt = 1
d=10:.1:100000
phase_diff = 4*pi*ht*hr./(landa*d) aproximación
Pr = Pt*((landa/(4*pi))^2)*((abs((sqrt(Gt)./d) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff)))./d)).^2)
Pr = Pt*((landa/(4*pi))^2)*(1./d).*((abs((sqrt(Gt)) + (R*sqrt(Gr).*(exp(-sqrt(-1)*phase_diff))))).^2)
figure(1), clf,
plot((d),10*log10(Pr/max(abs(Pr))))
grid
xlabel( log_1_0(d) )
ylabel( Potencia recibida (dBm) )
Platform: |
Size: 1024 |
Author: ramonmro |
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Description: C Programming and microcontrollers are two big topics, practically continental in size, and like continents, are easy to get lost in. Combining the two is a little like traipsing from Alaska to Tierra del Fuego.
Platform: |
Size: 3496960 |
Author: teophe |
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Description: Malla de puesta a tierra
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Size: 2048 |
Author: jcdespo |
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Description: Diseñ o de puesta a tierra
Platform: |
Size: 2048 |
Author: jcdespo |
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Description: Malla de puesta a tierra
Platform: |
Size: 4096 |
Author: jcdespo |
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Description: Puesta a tierra, electricidad, Malla a tierra
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Size: 1701888 |
Author: panchovedder |
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