1. Ch6: AM and BAM 6.1 Introduction AM: Associative Memory
BAM: Bidirectional Associative Memory
6.1 Introduction
。 Memory: store and deduce information
Memory can be pre-stored or trained
AM retrieves memory
in one shot
BAM retrieves memory
through many
iterations

2. 。 Categories of memory
LAM (Local Addressable Memory) -- give an address
to access the content pointed by the address.
CAM (Content Addressable Memory) -- give pieces
of information (key) to find the address where
stores the complete information, ex. hash table.

3. AM (Associative Memory) -- give a datum
(i) to find or recover the original noise–free data
(ii) to retrieve all the related data
Matrix Representation

4. Example:
Input vector:
Fault
tolerance
Recall:

5. 6.3 Types of AM Input vector: Output vector: Autoassociative memory －If
e.g., color correction, color constancy
Heteroassociative memory － If
e.g., i, Space transforms: Fourier transforms
ii, Dimensionality reduction: PCA

6. Interpolative associative memory －If
e.g., Compute
AM mapping is defined as
output input
Suppose
are orthonormal vectors.

7. a. Continuous-valued input patterns
i, Ideal pattern retrieval (noise-free)
e.g.,
ii, Noisy input
noise

8. b. Binary – valued input patterns: 0, 1
1. Memory matrix
where
i.e., W is formed from bipolar – valued
patterns: -1, 1
2. Threshold vector
i = 1, …, N (row sum)
3. Retrieval:
where
I : input vector,
O : output vector

9. 。 Example: Auto-association memory Two I/O pairs :
∵ Auto-AM
i, Memory matrix ,

13. ii, Threshold vector
1. First test pattern

14. Output pattern: 2. Second test pattern Output pattern:
nonlinear processing
Output pattern:
2. Second test pattern
Output pattern:

16. 6.4 BAM
Characteristics:

17. For autoassociative memory,

18. ‧ Output

19. 19

22. Second trial:

24. 6.4.2 Energy Function Dynamic system: a system whose state changes
with time.
State: a collection of adaptable quantitative and
qualitative items that characterizing the
system, e.g., weights, data flows …..

31. 31

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