3.6 a. Use the hybrid (binary/bipolar) form of Hebb rule learning as described in Example 3.6 to find the weight matrix for the associative memory network based on the following binary input-output vector pairs: s=0100 0 (=01 0 =01 001 @@= 0 s3) =0 1.0 0 t3)=0 1 s@=0 110 H4) =@ 1) b. Using the unit step function (with threshold 0) as the output units’ activation function, test the response of your network on each of the input patterns. Describe the results obtained. c. Test the response of your network on various combinations of input patterns with “‘mistakes” or ‘‘missing-data’ (as in Example 3.8). Discuss the results you ob- serve. 3.6 a. Use the hybrid (binary/bipolar) form of Hebb rule learning as described in Example3.6 to find the weight matrix for the associative memory network based on thefollowing binary input-output vector pairs:s(1) (1 0 0 0)t(1) = (1 0)s(2) = (1 0 0 1)t(2) = (1 0)s(3) = (0 1.0 0)t(3) =(0 1)s(4) = (01 1 0)t(4) = (0 1)b. Using the unit step function (with threshold 0) as the output units' activationfunction, test the response of your network on each of the input patterns. Describethe results obtained.c. Test the response of your network on various combinations of input patterns with"mistakes" or "missing data" (as in Example 3.8). Discuss the results you ob-serve.

ANN Terminology Before we classify the various learning rules in ANN, let us understand some…

Answered: 3.6 a. Use the hybrid (binary/bipolar)…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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3.6 a. Use the hybrid (binary/bipolar) form of Hebb rule learning as described in Example 3.6 to find the weight matrix for the associative memory network based on the following binary input-output vector pairs: s=0100 0 (=01 0 =01 001 @@= 0 s3) =0 1.0 0 t3)=0 1 s@=0 110 H4) =@ 1) b. Using the unit step function (with threshold 0) as the output units’ activation function, test the response of your network on each of the input patterns. Describe the results obtained. c. Test the response of your network on various combinations of input patterns with “‘mistakes” or ‘‘missing-data’ (as in Example 3.8). Discuss the results you ob- serve.

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