Fuzzy Membership Tool
أداة عضوية غامضة
ArcMap
ArcGIS
How to use Fuzzy Membership Tool in Arc Toolbox??
كيفية استخدام أداة عضوية غامضة ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Fuzzy Membership Tool, Overlay Toolset, Spatial Analyst Tools Toolbox
Fuzzy Membership
Transforms the input
raster into a 0 to 1 scale, indicating the strength of a membership in a set,
based on a specified fuzzification algorithm.
A value of 1 indicates
full membership in the fuzzy set, with membership decreasing to 0, indicating
it is not a member of the fuzzy set.
يحول البيانات النقطية المدخلة إلى مقياس من 0 إلى
1 ، مما يشير إلى قوة العضوية في مجموعة ، بناءً على خوارزمية fuzzification محددة.
تشير القيمة 1 إلى العضوية الكاملة في المجموعة
الغامضة ، مع انخفاض العضوية إلى 0 ، مما يشير إلى أنها ليست عضوًا في المجموعة
الغامضة.
Input raster
The input raster whose values will be scaled from 0 to 1.
It can be an integer or a floating-point raster.
Output raster
The output will be a floating-point raster with values ranging from 0 to
1.
Membership type (optional)
Specifies the algorithm used in fuzzification of the input raster.
Certain settings for Membership type employ a Spread parameter to
determine how rapidly the fuzzy membership values decrease from 1 to 0. The
default values for the spread are detailed in the table below.
· Gaussian—Assigns a membership value of 1 at the midpoint.The membership
decreases to 0 for values that deviate from the midpoint according to a normal
curve. Gaussian is similar to the Near function but has a more narrow spread.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary between [0.01–1].
· Small—Used to indicate that small values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Large—Used to indicate that large values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Near—Calculates memberships for values near some intermediate
value.Assigns a membership value of 1 at the midpoint. The membership decreases
to 0 for values that deviate from the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary within the range of
[0.001–1].
· MSLarge—Calculates membership based on the mean and standard deviation of
the input data where large values have high membership. The result can be
similar to the Large function, depending on how the multipliers of the mean and
standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· MSSmall—Calculates membership based on the mean and standard deviation of
the input data where small values have high membership. This is the default
membership type. The result can be similar to the Small function, depending on
how the multipliers of the mean and standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· Linear—Calculates membership based on the linear transformation of the
input raster. Assigns a membership value of 0 at the minimum and a membership
of 1 at the maximum.
o Minimum — Default is 1.
o Maximum — Default is 2.
Hedge (optional)
Defining a hedge increases or decreases the fuzzy membership values which
modify the meaning of a fuzzy set. Hedges are useful to help in controlling the
criteria or important attributes.
· NONE—No hedge is applied. This is the default.
· SOMEWHAT—Known as dilation, defined as the square root of the fuzzy
membership function. This hedge increases the fuzzy membership functions.
· VERY—Also known as concentration, defined as the fuzzy membership function
squared. This hedge decreases the fuzzy membership functions.
1.
Input raster أدخل البيانات النقطية
The input raster whose
values will be scaled from 0 to 1.
It can be an integer or
a floating-point raster.
البيانات النقطية المدخلة التي سيتم قياس قيمها من
0 إلى 1.
يمكن أن يكون عددًا صحيحًا أو نقطية فاصلة عشرية.
Output raster
The output will be a floating-point raster with values ranging from 0 to
1.
Membership type (optional)
Specifies the algorithm used in fuzzification of the input raster.
Certain settings for Membership type employ a Spread parameter to
determine how rapidly the fuzzy membership values decrease from 1 to 0. The
default values for the spread are detailed in the table below.
· Gaussian—Assigns a membership value of 1 at the midpoint.The membership
decreases to 0 for values that deviate from the midpoint according to a normal
curve. Gaussian is similar to the Near function but has a more narrow spread.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary between [0.01–1].
· Small—Used to indicate that small values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Large—Used to indicate that large values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Near—Calculates memberships for values near some intermediate
value.Assigns a membership value of 1 at the midpoint. The membership decreases
to 0 for values that deviate from the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary within the range of
[0.001–1].
· MSLarge—Calculates membership based on the mean and standard deviation of
the input data where large values have high membership. The result can be
similar to the Large function, depending on how the multipliers of the mean and
standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· MSSmall—Calculates membership based on the mean and standard deviation of
the input data where small values have high membership. This is the default
membership type. The result can be similar to the Small function, depending on
how the multipliers of the mean and standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· Linear—Calculates membership based on the linear transformation of the
input raster. Assigns a membership value of 0 at the minimum and a membership
of 1 at the maximum.
o Minimum — Default is 1.
o Maximum — Default is 2.
Hedge (optional)
Defining a hedge increases or decreases the fuzzy membership values which
modify the meaning of a fuzzy set. Hedges are useful to help in controlling the
criteria or important attributes.
· NONE—No hedge is applied. This is the default.
· SOMEWHAT—Known as dilation, defined as the square root of the fuzzy
membership function. This hedge increases the fuzzy membership functions.
· VERY—Also known as concentration, defined as the fuzzy membership function
squared. This hedge decreases the fuzzy membership functions.
2.
Output raster البيانات النقطية
المخرجة
The output will be a
floating-point raster with values ranging from 0 to 1.
سيكون الإخراج نقطيًا ذات فاصلة عائمة بقيم تتراوح
من 0 إلى 1.
Membership type (optional)
Specifies the algorithm used in fuzzification of the input raster.
Certain settings for Membership type employ a Spread parameter to
determine how rapidly the fuzzy membership values decrease from 1 to 0. The
default values for the spread are detailed in the table below.
· Gaussian—Assigns a membership value of 1 at the midpoint.The membership
decreases to 0 for values that deviate from the midpoint according to a normal
curve. Gaussian is similar to the Near function but has a more narrow spread.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary between [0.01–1].
· Small—Used to indicate that small values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Large—Used to indicate that large values of the input raster have high
membership in the fuzzy set.Assigns a membership value of 0.5 at the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 5.
· Near—Calculates memberships for values near some intermediate
value.Assigns a membership value of 1 at the midpoint. The membership decreases
to 0 for values that deviate from the midpoint.
o Midpoint — Default is the midpoint of the range of values of the input
raster.
o Spread — Default is 0.1. Typically, the values vary within the range of
[0.001–1].
· MSLarge—Calculates membership based on the mean and standard deviation of
the input data where large values have high membership. The result can be
similar to the Large function, depending on how the multipliers of the mean and
standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· MSSmall—Calculates membership based on the mean and standard deviation of
the input data where small values have high membership. This is the default
membership type. The result can be similar to the Small function, depending on
how the multipliers of the mean and standard deviation are defined.
o Mean multiplier — Default is 1.
o Standard deviation multiplier — Default is 2.
· Linear—Calculates membership based on the linear transformation of the
input raster. Assigns a membership value of 0 at the minimum and a membership
of 1 at the maximum.
o Minimum — Default is 1.
o Maximum — Default is 2.
Hedge (optional)
Defining a hedge increases or decreases the fuzzy membership values which
modify the meaning of a fuzzy set. Hedges are useful to help in controlling the
criteria or important attributes.
· NONE—No hedge is applied. This is the default.
· SOMEWHAT—Known as dilation, defined as the square root of the fuzzy
membership function. This hedge increases the fuzzy membership functions.
· VERY—Also known as concentration, defined as the fuzzy membership function
squared. This hedge decreases the fuzzy membership functions.
3.
Membership type (optional) نوع
العضوية (اختياري)
Specifies the algorithm
used in fuzzification of the input raster.
Certain settings for
Membership type employ a Spread parameter to determine how rapidly the fuzzy
membership values decrease from 1 to 0. The default values for the spread are
detailed in the table below.
·
Gaussian—Assigns a membership value of 1 at the midpoint.The
membership decreases to 0 for values that deviate from the midpoint according
to a normal curve. Gaussian is similar to the Near function but has a more
narrow spread.
oMidpoint — Default is the midpoint of the range
of values of the input raster.
oSpread — Default is 0.1. Typically, the values
vary between [0.01–1].
·
Small—Used to indicate that small values of the input raster have
high membership in the fuzzy set.Assigns a membership value of 0.5 at the
midpoint.
oMidpoint — Default is the midpoint of the range
of values of the input raster.
oSpread — Default is 5.
·
Large—Used to indicate that large values of the input raster have
high membership in the fuzzy set.Assigns a membership value of 0.5 at the
midpoint.
oMidpoint — Default is the midpoint of the range
of values of the input raster.
oSpread — Default is 5.
·
Near—Calculates memberships for values near some intermediate
value.Assigns a membership value of 1 at the midpoint. The membership decreases
to 0 for values that deviate from the midpoint.
oMidpoint — Default is the midpoint of the range
of values of the input raster.
oSpread — Default is 0.1. Typically, the values
vary within the range of [0.001–1].
·
MSLarge—Calculates membership based on the mean and standard
deviation of the input data where large values have high membership. The result
can be similar to the Large function, depending on how the multipliers of the
mean and standard deviation are defined.
oMean multiplier — Default is 1.
oStandard deviation multiplier — Default is 2.
·
MSSmall—Calculates membership based on the mean and standard
deviation of the input data where small values have high membership. This is
the default membership type. The result can be similar to the Small function,
depending on how the multipliers of the mean and standard deviation are
defined.
oMean multiplier — Default is 1.
oStandard deviation multiplier — Default is 2.
·
Linear—Calculates membership based on the linear transformation of
the input raster. Assigns a membership value of 0 at the minimum and a
membership of 1 at the maximum.
oMinimum — Default is 1.
oMaximum — Default is 2.
يحدد الخوارزمية المستخدمة في تشويش مدخلات
البيانات النقطية.
تستخدم إعدادات معينة لنوع العضوية معلمة Spread
لتحديد مدى سرعة انخفاض قيم العضوية المشوشة من 1 إلى 0. تم تفصيل القيم
الافتراضية للحيز في الجدول أدناه.
• Gaussian - يخصص قيمة العضوية 1 عند نقطة المنتصف. تقل العضوية إلى 0 للقيم
التي تنحرف عن نقطة الوسط وفقًا لمنحنى عادي. Gaussian مشابه للدالة القريبة ولكن له انتشار أضيق.
o نقطة
المنتصف - الافتراضي هي نقطة المنتصف لنطاق قيم البيانات النقطية المدخلة.
o
السبريد - الافتراضي هو 0.1. عادة ، تختلف القيم بين [0.01 - 1].
• صغير - يستخدم للإشارة
إلى أن القيم الصغيرة للمدخل النقطي لها عضوية عالية في المجموعة الغامضة. يعيّن
قيمة عضوية قدرها 0.5 عند نقطة المنتصف.
o نقطة
المنتصف - الافتراضي هي نقطة المنتصف لنطاق قيم البيانات النقطية المدخلة.
o
السبريد - الافتراضي هو 5.
• كبير - يستخدم للإشارة
إلى أن القيم الكبيرة للمدخلات النقطية لها عضوية عالية في المجموعة الغامضة.
يعيّن قيمة عضوية قدرها 0.5 عند نقطة المنتصف.
o نقطة
المنتصف - الافتراضي هي نقطة المنتصف لنطاق قيم البيانات النقطية المدخلة.
o
السبريد - الافتراضي هو 5.
• قريب - لحساب العضويات
للقيم القريبة من بعض القيم المتوسطة. يعيّن قيمة العضوية 1 عند نقطة المنتصف. تقل
العضوية إلى 0 للقيم التي تنحرف عن نقطة الوسط.
o نقطة
المنتصف - الافتراضي هي نقطة المنتصف لنطاق قيم البيانات النقطية المدخلة.
o
السبريد - الافتراضي هو 0.1. بشكل نموذجي ، تختلف القيم في نطاق [0.001-1].
• MSLarge - لحساب العضوية بناءً على المتوسط والانحراف المعياري لبيانات
الإدخال حيث يكون للقيم الكبيرة عضوية عالية. يمكن أن تكون النتيجة مماثلة للدالة
الكبيرة ، اعتمادًا على كيفية تحديد مضاعفات المتوسط والانحراف المعياري.
o متوسط
المضاعف - الافتراضي هو 1.
o مضاعف
الانحراف المعياري - الافتراضي هو 2.
• MSSmall - لحساب العضوية بناءً على المتوسط والانحراف المعياري لبيانات
الإدخال حيث يكون للقيم الصغيرة عضوية عالية. هذا هو نوع العضوية الافتراضي. يمكن
أن تكون النتيجة مماثلة للدالة الصغيرة ، اعتمادًا على كيفية تحديد مضاعفات
المتوسط والانحراف المعياري.
o متوسط
المضاعف - الافتراضي هو 1.
o مضاعف
الانحراف المعياري - الافتراضي هو 2.
• خطي - لحساب العضوية
بناءً على التحويل الخطي لمدخل البيانات النقطية. يعيّن قيمة عضوية تبلغ 0 كحد
أدنى وعضوية 1 كحد أقصى.
o الحد
الأدنى - الافتراضي هو 1.
o الحد
الأقصى - الافتراضي هو 2.
Hedge (optional)
Defining a hedge increases or decreases the fuzzy membership values which
modify the meaning of a fuzzy set. Hedges are useful to help in controlling the
criteria or important attributes.
· NONE—No hedge is applied. This is the default.
· SOMEWHAT—Known as dilation, defined as the square root of the fuzzy
membership function. This hedge increases the fuzzy membership functions.
· VERY—Also known as concentration, defined as the fuzzy membership function
squared. This hedge decreases the fuzzy membership functions.
4.
Hedge (optional) التحوط (اختياري)
Defining a hedge
increases or decreases the fuzzy membership values which modify the meaning of
a fuzzy set. Hedges are useful to help in controlling the criteria or important
attributes.
·
NONE—No hedge is applied. This is the default.
·
SOMEWHAT—Known as dilation, defined as the square root of the
fuzzy membership function. This hedge increases the fuzzy membership functions.
·
VERY—Also known as concentration, defined as the fuzzy membership
function squared. This hedge decreases the fuzzy membership functions.
يؤدي تحديد التحوط إلى زيادة أو تقليل قيم العضوية
غير الواضحة التي تعدل معنى المجموعة الغامضة. التحوطات مفيدة للمساعدة في التحكم
في المعايير أو السمات الهامة.
• لا شيء - لا يتم تطبيق
التحوط. هذا هو الافتراضي.
• SOMEWHAT - يُعرف بالتمدد ، ويُعرّف بأنه الجذر التربيعي لدالة العضوية
الغامضة. يزيد هذا التحوط من وظائف العضوية الغامضة.
• VERY -
يُعرف أيضًا بالتركيز ، ويُعرّف على أنه مربع دالة العضوية الغامضة. يقلل هذا
التحوط من وظائف العضوية المبهمة.
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