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Nông Thị Hoa và Đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 113(13): 61 - 65

61

AN IMPROVED LEARNING ALGORITHM OF BAM

Nong Thi Hoa1,*, Bui The Duy2

1College of Information Technology and Communication – TNU

2Human Machine Interaction Laboratory – Vietnam National University, Hanoi

SUMMARY

Artificial neural networks, characterized by massive parallelism, robustness, and learning capacity,

have many applications in various fields. Bidirectional Associative Memory (BAM) is a neural

network that is extended from Hopfield networks to make a two-way associative search for a

pattern pair. The most important advantage of BAM is recalling stored patterns from noisy inputs.

Learning process of previous BAMs, however, is not flexible. Moreover, orthogonal patterns are

recalled better than other patterns. It means that, some important patterns cannot be recalled. In

this paper, we propose a learning algorithm of BAM, which learns from training data more flexibly

as well as improves the ability of recall for non-orthogonal patterns. In our learning algorithm,

associations of patterns are updated flexibly in a few iterations by modifying parameters after each

iteration. Moreover, the proposed learning algorithm assures the recalling of all patterns is similar,

which is presented by the stop condition of the learning process. We have conduct experiments

with five datasets to prove the effectiveness of BAM with the proposed learning algorithm (FBAM

- Flexible BAM). Results from experiments show that FBAM recalls better than other BAMs in

auto-association mode.

Keywords: Bidirectional Associative Memory, Associative Memory, Learning Algorithm, Noise

Tolerance, Pattern Recognition.

INTRODUCTION*

Artificial neural networks, characterized by

massive parallelism, robustness, and learning

capability, effectively solve many problems

such as pattern recognition, designing

controller, clustering data. BAM [1] is

designed from two Hopfield neural networks

to show a two-way associative search of

pattern pairs. An important advantage of

BAM is recalling stored patterns from noisy

or partial inputs. Moreover, BAM possesses

two attributes overcome other neural

networks. First, BAM is stable without

condition. Second, BAM converges to a

stable state in a synchronous mode.

Therefore, it is easy to apply BAM for real

applications.

Studies on models of BAM can be divided

into two categories: BAMs without iterative

learning and BAMs with iterative learning

(BAMs with multiple training strategy).

BAMs with iterative learning recall more

*

Tel: 01238492484

effectively than BAMs without iterative

learning. The iterative learning of BAMs is

shown into two types. The first type is using

the minimum number of times for training

pairs of patterns (MNTP). BAMs [2, 3, 4]

showed multiple training strategy which

assured orthogonal patterns were recalled

perfectly. However, the learning process is

not flexible because MNTP is fixed. The

second type is learning pairs of patterns in

many iterations. BAMs learned pairs of

patterns sequentially in many iterations to

guarantee the perfect recall of orthogonal

patterns [5, 6, 7, 8]. Additionally, new

weights of associations depend on old weights

in a direct way. Therefore, it takes a long time

to modify weights if old weights are far from

desired values. In other words, previous

BAMs recall non-orthogonal patterns weakly

and learn fixedly. In this paper, we propose an

iterative learning algorithm of BAM, which

learns more flexibly as well as improves the

ability of recall for non-orthogonal patterns.

We use MNTP to show the multiple training

strategy. In the proposed learning rule,