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. | Nông Thị Hoa và Đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 113(13): 61 - 65 AN IMPROVED LEARNING ALGORITHM OF BAM Nong Thi Hoa1,*, Bui The Duy2 1 College of Information Technology and Communication – TNU 2 Human 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 .
