?????? FUZZY ASSOCIATIVE MEMMORIES?

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??????FUZZY ASSOCIATIVE MEMMORIES?

• Presented by Yang Baisheng
• E.E. Dept.
• Xidian University

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OUTLINE

• Fuzzy Hebb FAMs(?)
• 6.Binary Input-Output FAMs
• 7. Multiantecedent FAM Rules
• 8. Adaptive Decompositional Inference
• Adaptive FAMs (Product-Space Clustering in FAM
  Cells)
• 1.Adaptive FAM-Rule Generation
• 2.Adaptive BIOFAM Clustering
• 3.Adaptive BIOFAM Example
• Inverted Pendulum

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Binary Input-Output FAMs

• BIOFAMs map system-variable to control,
  classification, or other output data.
• For example
• A BIOFAM maps traffic densities to screen
  (and red) light durations.
• In inverted-pendulum example, the system
• maps the system-variable (
  ) to control data ( ).

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Multiantecedent FAM Rules (???FAM??)

• 1.Consider the FAM rule IF X is A, THEN C is
  Z, or for short.
• 2.The rule is IF X is A AND Y is B, THEN C is
  Z, or for short.

What to do?
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Multiantecedent FAM Rules (???FAM??)

• 2 Single-antecedent FAMs
• Multiantecedent FAM Rules

Defuzzify it to yield the exact output.
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Multiantecedent FAM Rules

• Suppose we present the exact inputs , to
  the single-FAM-rule system that
  stores(A,BC).
• We present the unit bit vectors and to
  as nonfuzzy set inputs.Then

Property of Hebb Matrix
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Multiantecedent FAM Rules

• Representing with its membership function
• For all in

BIOFAM prescription
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Multiantecedent FAM Rules
IF we encode and with
correlation-product encoding, decompositional
inference gives the BIOFAM version of
correlation-product inference
Correlation-Product Encoding

• Also, We can get the FAM rules

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Adaptive Decompositional Inference

• Let define an arbitrary
  neural-network system that maps fuzzy subset
  of to fuzzy subsets of .
  can define a different
  neural-network.

The neural-network change with time.
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Adaptive FAMs(Product-Space Clustering in FAM
Cells)

• Adaptive FAM-Rule Generation
• Adaptive BIOFAM Clustering
• Adaptive BIOFAM Example
• Inverted Pendulum

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Adaptive FAM-Rule Generation

• Let denote quantization
  vectors in the input-output product space
  .
• We count the number of quantizing vectors in
  each FAM cell .

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Adaptive BIOFAM Clustering

• Through neural-network learning algorithm,
  learn to distribute input-output data in the
  input-output product space. Data clusters reflect
  FAM rules, such as the steady-state FAM rule IF
  is ZE AND is ZE, THEN is ZE.

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BIOFAM?????????
???? ? ???? (????????)
?????? (????) ? ???? (????)
??????? ????, ????? ????, ?????????
?????, ??FAM??
??? DCLAVQ ??????
????? ??? ???? ??????, ?????????
????? ?? ????

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Inverted Pendulum

• 1????????????
• ????????????
• ??
• ????????
• ?????????????
• 2?????
• ???????MATLAB?????????????
• 1000???????????????

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Membership Function
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FAM Bank Synaptic Histogram
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FAM Rules
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FAM Rules

• ???
• ???????????????-2.18?

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??????
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????

• scope

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FAM Rules
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FAM Rules
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REFERENCE

• ??FAM???????????? ???,??? 2003.08
• ??????????????????? ? ? 2004.09
• ?????????? ???????? ???,???,??? 2000.11
• ??ANN???????????????? ???,? ?,??? 1997.04

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REFERENCE

• ?BP???????????????????? ? ?,? ?,??? 1996.11
• ??????????????????????? ??? 1997.08

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THANKS!