Fourth International Symposium on Neural Networks (ISNN) June 3-7, 2007, Nanjing, China A Hierarchical Self-organizing Associative Memory for Machine Learning Janusz A. Starzyk, Ohio University Haibo He, Stevens Institute of Technology Yue Li, O2 Micro Inc
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Fourth International Symposium on Neural Networks (ISNN) June 3-7, 2007, Nanjing, China
A Hierarchical Self-organizing Associative Memory forMachine Learning
Janusz A. Starzyk, Ohio UniversityHaibo He, Stevens Institute of Technology
Characteristics: Self-organization; Sparse and local interconnections; Feedback propagation; Information inference; Hierarchical organization; Robust and self-adaptive; Capable of both hetero-associative (HA) and auto-associative (AA)
Feed forward only Feed forward
Feed backward
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Outline
Introduction;
Associative learning algorithm;
Memory network architecture and operation;
Simulation analysis;
Conclusion and future research;
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Basic learning element
Self-determination of the function value:
An example:
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Signal strength (SS)
Signal strength (SS) =| Signal value – logic threshold|
(SS range: [0, 1])
Provides a coherent way to determine when to trigger an association; Helps to resolve multiple feedback signals;
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Three types of associations
IOA: Input only association;
OOA: Output only association;
INOUA: Input-output association;
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Probability based associative learning algorithm
Case 1: Given the values of both inputs, decide the output value;
0021
2110
21
21
01
21
2111
21
21
)0,0(
)1,0,0(
)0,1(
)1,0,1(
)1,0(
)1,1,0(
)1,1(
)1,1,1()(
VIIp
FIIpV
IIp
FIIp
VIIp
FIIpV
IIp
FIIpOV
•==
===+•==
===+
•==
===+•==
====
)1)(1();1(
;)1(;
0010
0111
nmVnmV
nmVmnV
−−=−=−==
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Probability based associative learning algorithm
Case 2: Given the values of one input and an un-defined output, decide the value of the other input;
( ) ( )( )1
1
211
1
212 1
)0(
)1,0(
)1(
)1,1()( IV
Ip
IIpIV
Ip
IIpIV −•
===+•
====
01001
11101
)0(
)1(
ppIp
ppIp
+==+==
For instance:
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Probability based associative learning algorithm
Case 3: Given the values of the output, decide the values of both inputs;
( ) ( )( )
( ) ( )( )OVFp
IFpOV
Fp
IFpIV
OVFp
IFpOV
Fp
IFpIV
−•=
==+•=
===
−•=
==+•=
===
1)0(
)1,0(
)1(
)1,1()(
1)0(
)1,0(
)1(
)1,1()(
222
111
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Probability based associative learning algorithm
Case 4: Given the values of one input and the output, decide the other input value;