Artificial Neural Network Bidirectional Associative Memory BAM Introduction

-Artificial Neural Network. Bidirectional Associative Memory (BAM) 朝陽科技大學 資訊管理系 李麗華 教授

Introduction • Bidirectional Associative Memory (BAM) was proposed by Bart Kosko in 1985. • It is a hetro-associative memory network. • It allows the network to memorize from a set of pattern Xp to recall another set of pattern Yp Y 1 Y 2 ‧‧‧‧‧‧ Ym ‧‧‧‧‧‧‧ 朝陽科技大學 李麗華 教授 2

Assoicative Memory (AM) 1 • Def: Associative memory (AM) is any device that associates a set of predefined output patterns with specific input patterns. • Two types of AM: – Auto-associative Memory: Converts a corrupted input pattern into the most resembled input. – Hetro-associative Memory: Produces an output pattern that was stored corresponding to the most similar input pattern. 朝陽科技大學 李麗華 教授 3

Assoicative Memory (AM) 2 Models: It is the associative mapping of an input vector X into the output vector V. EX: Hopfield Neural Network (HNN) EX: Bidirectional Associative Memory (BAM) X 1 v 1 X 2 v 2 X 3 : Assoicative Memory v 3 : vm Xn 朝陽科技大學 李麗華 教授 4

BAM Architecture ① Input layer： ② Output layer： ③ Weights： Y 1 ④ Connection： Y 2 ‧‧‧‧‧‧ Ym It’s a 2 -layer, fully connected, feed forward & feed back network. ‧‧‧‧‧‧‧ 朝陽科技大學 李麗華 教授 5

BAM Architecture (cont. ) ⑤ Transfer function： 朝陽科技大學 李麗華 教授 6

BAM Example(2/4) 1. Learning – – Set up network Setup weights Y 1 Y 2 Y 3 Y 4 1 -1 1 -1 -1 1 -1 1 1 1 -1 -1 -1 1 -1 -1 1 1 朝陽科技大學 李麗華 教授 8

BAM Example(3/4) 2. Recall ①Read network weights ②Read test pattern ③Compute Y ④ Compute X ⑤ Repeat (3) & (4) until converge 朝陽科技大學 李麗華 教授 9