National Taiwan Ocean University Department of Communications, Navigation and Control Engineering

cartcletchΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

63 εμφανίσεις

Speaker
:游佳龍

ID

19967034

Date

11/24/2010


National Taiwan Ocean University

Department of Communications, Navigation and
Control Engineering

Outline


Abstract


Introduction


The extended BAM Neural Network Model


Proof of the New Model’s Stability


Experiment Results

Abstract


In this paper we propose an extended bidirectional associative
memory (BAM) neural network model which can do auto
-

and
hetero
-
associative memory. The theoretical proof for this neural
network model’s stability is given. Experiments show that this
neural network model is much more powerful than the M
-
P
Model, Discrete Hopfield Neural Network, Continuous
Hopfield Neural Network, Discrete Bidirectional Associative
Memory Neural Network, Continuous and Adaptive
Bidirectional Associative Memory Neural Network, Back
-
Propagation Neural Network and Optimal Designed Nonlinear
Continuous Neural Network. Experimental results also show
that, when it does auto
-
associative memory, the power of this
model is the same as the Loop Neural Network Model which
can only do auto
-
associative memory.

Introduction


Associative

memory

is

an

important

part

in

neural

network

theory

and

it

is

also

an

efficient

function

in

the

applications

of

intelligent

control,

pattern

recognition

and

artificial

intelligence
.


At

present,

many

neural

network

models

such

as

Loop

model,

M
-
P

model,

Discrete

and

Continuous

Hopfield

Model,

Kosko’s

Discrete

BAM,

Optimal

Designed

Nonlinear

Continuous

Neural

Network,

etc
.
,

which

can

do

associative

memory,

have

existed
.

Each

model

has

its

own

advantages

and

disadvantages
.



Introduction


In practical applications, the more powerful the
network is, the better the associative memory result
are. One important task is to find or construct a
powerful associative neural network. The so
-
called
neural network model has two meanings, that is its
structure and its training algorithm.


In this paper we propose an extended bidirectional
associative memory(BAM) neural network model.
The reason why we call this new model an extended
BAM neural network model is that its structure is the
same as the BAM model. The different between the
BAM and the extended BAM is the training algorithm.


The extended BAM

Neural Network Model


This part introduces the architecture and learning
algorithm for the Extended. This model can be used to
carry out both auto
-
associative memory and hetero
-
associative memory. The BAM model(Kosk0 Model)
is a memory consisting of two layers. It uses the
forward and backward information flow to produce an
associative search for stored stimulus
-
response
association
.

The extended BAM

Neural Network Model

The extended BAM

Neural Network Model


The firing function for both 1ayers:neuron is





Consider the stored association pairs as



The formula for the weight matrix is


For our extended BAM model, the learning algorithm
is Delta Learning Rule.



Delta learning rule


During training we treat this two layer network as a
feedforward neural network, and the activation function for
output layer's neurons is sigmoid function.




After the training is finished, we use the following activation
function in both layers to do associative memory.




By this training method the forward connection weight matrix
M can be obtained. We use
M
as the backward connection
weight matrix.

The extended BAM

Neural Network Model

Proof of the New Model’s
Stability


We can define the energy function as



Since we get the energy
function equivalent form as follows



The energy change due to the state change
of a is

Proof of the New Model’s
Stability


By the BAM theorem


has only three values, i.e.,
-
2,0 and 2. If , we have
So,


and


So, we have This is
the situation of zero change in and we don’t consider this
case. The energy change due to the state change of


is the same as . Hence along discrete trajectories
as claimed.

Proof of the New Model’s
Stability


Since E is bounded below




the associative memory of the extended BAM converges
to some stable points, meaning that, the network is stable.

Experiment Result


The experiment results show that the New Model is much
more powerful than the other models to carry out
associative memory.


In our experiment the network consists of 8 processing
units(neurons) for each layer. The set of vector pairs to
be stored is




The experiments are carried out in the following four
cases.


Experiment Result

Experiment Result

Experiment Result


Using the same method as above, we get the following
results.

References