Associative Learning Memories -SOLAR_A Matlab code presentation.
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Transcript of Associative Learning Memories -SOLAR_A Matlab code presentation.
Associative Learning Memories -SOLAR_A
Matlab code presentation
Introduction
Associating SOLAR (SOLAR_A)
SOLAR_A structures are hierarchically organized and have ability to classify patterns in a network of sparsely connected neurons.
Association Training Neurons learn associations between
pattern and its code. Once the training is completed, a network is capable to make necessary associations.
Testing When the network is presented with the
pattern only, it drives the associated input signals to these code values that represent the observed pattern.
Signal definition
The inner signals in the network range from 0 to 1. A signal is a determinate low or determinate high if its value is 0 or 1.
0 - 0.5 weak low 0.5 - 1 weak high 0.5 “inactive”, or “high impedance”
Neurons’ definitions
If a neuron is able to observe any type of statistical correlations of its input connections, it will function as an associative neuron.
Otherwise it will be a transmitting neuron.
Associative neuron
A neuron is called an associative neuron when its inputs I1 and I2 are associated
Inputs I1 and I2 are associated if and only if I2 can be implied from I1 and I1 can be implied from I2 simultaneously.
associative neuron
I1 and I2 are inputs an associative neuron has received in training.
It is quite clear that I1 and I2 are most likely to be simultaneously low or high although there is some noise.
This can be verified using P(I2 | I1) and P(I1 | I2), and implying values I2 from I1 and I1 from I2.
5.0Iif1,
5.0Iif0,)I,I(
1
1215f
Low I1 is associated with low I2, and high I1 is associated with high I2.
Network Structure
Hierarchical structure
In horizontal direction, the neurons on one layer can only connect to the neurons on the previous layer.
Network Structure
The connection in vertical direction obeys 80% Gaussian distribution with standard deviation 2
+ 20% uniform distribution
Network Structure
The network uses feedback signals to pass information backwards to the associated inputs.
Testing
During testing, the missing parts of the data need to be recovered from the existing data through association.
For example, in a pattern recognition problem, the associated code inputs are unknown and therefore set to 0.5.
Neuron Feedback Scheme
Iris Plants Database The Iris database has:
3 classes (Iris Setosa, Iris Versicolour and Iris Virginica)
4 numeric attributes (petal length, petal width , sepal length , sepal width )
150 instances of 50 instances for each class, where each class refers to a type of iris plant.
The classification objective Identify the class ID based on the input feature
(attribute) values
Coding of the database The 4 features were scaled linearly and cod
ed using a sliding bar code .
Input bits from (V-Min)+1 to (V-Min)+L will be set high and remaining bits will be low
N-L=Max-Min
N
LV-Min
Coding of the database
We scaled the 4 features of Iris database between 0-30, and
Set the length of L equal to 12 The total length of each feature is 42
The feature input requires 168 bits
Coding of the database
In order to increase the probability that each feature is associated with sample class code, we merged the 4 features.
Coding of the database
Coding of the database
There are 3 classes total
We use 3M bits to code the class ID maximizing their code Hamming distance
The white part is filled by 2M-bit 0 string, while the grey part is filled by M-bit 1 string.
Iris database simulation
Rows 1-168 Features
Rows 174-203 class ID
Iris database simulation
Glass identification database
0 2 4 6 8 10 12
5
10
15
20
Number of associative neurons per layer
Layers
Num
ber
of a
ssoc
iativ
e ne
uron
s
Simulation of mixed features and class ID
code C
lass
ID
Fe
atu
re
Fe
atu
re
Fe
atu
reC
lass
ID
Cla
ss I
D
Cla
ss I
D
Cla
ss I
DF
ea
ture
Fe
atu
re
Simulation of mixed features and class ID code
Iris databaseIris database
Image recovery
Examples of training patterns
Testing results and recovered images of letter B and J
Coding example
Samples from Iris database 5.1,3.5,1.4,0.2,Iris-setosa(class 1) 7.0,3.2,4.7,1.4,Iris-versicolor (class
2) 6.3,3.3,6.0,2.5,Iris-virginica (class
3)
Coding example Coding:5.1,3.5,1.4,0.2,Iris-setosa (class
1) Pre-preparing: 51,35,14,2,1 Scaling the features (51,35,14,2)
from 0 to 30 After scaling: 7,19,2,2,1
Coding example Features 7 000000011111111111100000000000000000000000 19 000000000000000000011111111111100000000000 2 001111111111110000000000000000000000000000 2 001111111111110000000000000000000000000000 Class ID 1 1111111111…1110000000…0000000000000…000
56 bits 112 bits
12 bits7 bits
Coded data Matrix- Input
Features Class ID code
Input matrix
M Training data
N Testing data
Matlab user interface
main.m – main function training2.m – training function testing2.m – testing function catchassociating.m– actively associati
ve neurons generate_input– coding the database
parameters
columns- depth of layers rows- length of an input pattern stdr- standard deviation in vertical stdc- standard deviation in horizontal n_tests- test numbers
training.m r_distribution(meanr,stdr,rows,column
s,width) --defines distribution in vertical directio
n
normrnd(meanr,stdc,rows,columns) --defines distribution in horizontal direc
tion