fuzzyinference(sets)
fuzzyinference()所属R语言包：sets
Fuzzy inference
模糊推理
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Basic infrastructure for building and using fuzzy inference
基础设施的建设和使用模糊推理
用法----------Usage----------
fuzzy_inference(system, values, implication = c("minimum", "product"))
fuzzy_rule(antecedent, consequent)
fuzzy_system(variables, rules)
fuzzy_partition(varnames, FUN = fuzzy_normal, universe = NULL, ...)
fuzzy_variable(...)
x %is% y
参数----------Arguments----------
参数：...
For fuzzy_variable: named list of fuzzy sets (or membership functions from which the fuzzy sets will be created using the default universe). For fuzy_partition: further arguments passed to FUN.
对于fuzzy_variable：命名的模糊集（或将使用默认的宇宙的模糊子集的隶属度函数）的列表。对于fuzy_partition：进一步参数传递给FUN。
参数：antecedent, consequent
parts of an inference rule (see details).
部分的推理规则（见详情）。
参数：variables
Set or tuple of fuzzy variables (note that tuples must be used if two variables have the same definition).
设置或元组的模糊变量（注意，如果两个变量具有相同的定义，必须使用元组）。
参数：rules
Set of rules.
设置的规则。
参数：system
A fuzzy system.
模糊系统。
参数：values
Named list of input values to the system. The names must match the labels of the variable set.
提及的输入值的列表的系统。名称必须符合变量集的标签。
参数：implication
A vectorized function taking two arguments, or a character string indicating the parallel minimum or the product function.
一个矢量函数有两个参数，或者是一个字符串表示的并行最低或产品的功能。
参数：varnames
Either a character vector of fuzzy category labels, to be used with the default locations, or a named numeric vector of locations.
一个字符向量的模糊类别标签，将使用默认的位置，或一个名为“数字矢量的位置。
参数：FUN
Function generator for membership functions to be used for the fuzzy partition.
隶属函数的函数发生器，用于使用模糊分区。
参数：universe
Universal set used for computing the memberships grades.
通用设置用于计算的会员资格等级。
参数：x
The name of a fuzzy variable.
一个模糊变量的名称。
参数：y
The name of a category, belonging to a fuzzy variable.
一类的名称，属于一个模糊变量。
Details
详细信息----------Details----------
These functions can be used to create simple fuzzy inference machines based on fuzzy (“linguistic”) variables and fuzzy rules. This involves five steps:
这些功能可以用来创建简单的基于模糊（“语言”）变量和模糊规则的模糊推理机。这包括五个步骤：
Fuzzification of the input variables.
输入变量的模糊化。
Application of fuzzy operators (AND, OR, NOT) in the antecedents of some given rules.
应用模糊算子（AND，OR，NOT）在某些给定的规则的来路。
Implication from the antecedent to the consequent.
随之而来的从先前的启示。
Aggregation of the consequents across the rules.
整个规则的后件的聚集。
Defuzzification of the resulting fuzzy set.
去模糊化，将所得的模糊集。
Implication is based on either the minimum or the product. The evaluation of the logical expressions in the antecedents, as well as the aggregation of the evaluation result for each single rule, depends on the fuzzy logic currently set.
言下之意是最小或产品的基础上。来路，以及单一的规则评价结果为每个聚集中的逻辑表达式的评价，取决于当前设置的模糊逻辑。
值----------Value----------
For fuzzy_inference: a generalized set. For fuzzy_rule and fuzzy_system: an object of class fuzzy_rule and fuzzy_system, respectively. For fuzzy_variable and fuzzy_partition: an object of class fuzzy_variable, inheriting from tuple.
fuzzy_inference：广义集。对于fuzzy_rule和fuzzy_system：对象类fuzzy_rule和fuzzy_system，分别。 fuzzy_variable和fuzzy_partition：类fuzzy_variable，继承自tuple的对象。
参见----------See Also----------
set and gset for the set types, fuzzy_tuple for available fuzzy functions, and fuzzy_logic on the behavior of the implemented fuzzy operators.
set和gset的集合类型，fuzzy_tuple可用模糊函数，和fuzzy_logic实现的模糊算子的行为。
实例----------Examples----------
## set universe[＃设定宇宙]
sets_options("universe", seq(from = 0, to = 25, by = 0.1))
## set up fuzzy variables[＃设置模糊变量]
variables <-
set(service = fuzzy_partition(varnames = c(poor = 0, good = 5, excellent = 10), sd = 1.5),
food = fuzzy_variable(rancid = fuzzy_trapezoid(corners = c(-2, 0, 2, 4)),
delicious = fuzzy_trapezoid(corners = c(7, 9, 11, 13))),
tip = fuzzy_partition(varnames = c(cheap = 5, average = 12.5, generous = 20),
FUN = fuzzy_cone, radius = 5)
)
## set up rules[＃设置规则]
rules <-
set(
fuzzy_rule(service %is% poor || food %is% rancid, tip %is% cheap),
fuzzy_rule(service %is% good, tip %is% average),
fuzzy_rule(service %is% excellent || food %is% delicious, tip %is% generous)
)
## combine to a system[＃结合到一个系统]
system <- fuzzy_system(variables, rules)
print(system)
plot(system) ## plots variables[＃图变量中]
## do inference[＃做的推断]
fi <- fuzzy_inference(system, list(service = 3, food = 8))
## plot resulting fuzzy set[＃图产生的模糊集]
plot(fi)
## defuzzify[＃逆模糊化]
gset_defuzzify(fi, "centroid")
## reset universe[＃重置宇宙]
sets_options("universe", NULL)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-9-30 01:33:42
