From 715bb368fe6e3a02770d32c517d4eccf8c351f59 Mon Sep 17 00:00:00 2001 From: Shradha Sehgal Date: Sun, 24 Mar 2019 15:01:23 +0530 Subject: [PATCH 1/4] Made images responsive --- src/lab/exp8/Observations.html | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/lab/exp8/Observations.html b/src/lab/exp8/Observations.html index 218e76f35..eb9a5c58c 100644 --- a/src/lab/exp8/Observations.html +++ b/src/lab/exp8/Observations.html @@ -120,10 +120,7 @@

Solution of optimization problems

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  • Observe the output of the network for different number of cities. Start from a small number of cities (such as 10), and go up From 16eec4b6aff2581749767316a56dee97688d7569 Mon Sep 17 00:00:00 2001 From: Shradha Sehgal Date: Sun, 24 Mar 2019 15:10:59 +0530 Subject: [PATCH 2/4] Made GIFs responsive --- src/lab/exp8/Illustration.html | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/lab/exp8/Illustration.html b/src/lab/exp8/Illustration.html index c90c6ced7..13351f9fe 100644 --- a/src/lab/exp8/Illustration.html +++ b/src/lab/exp8/Illustration.html @@ -117,10 +117,10 @@

    Application of SOM to travelling salesman problem

    For 100 cities and a SOM with 1000 neurons in the output layer

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    Figure 1: Kohonen's self-organization feature map for TSP for 100 cities and 1000 units in the output layer.
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    Effect of varying the number of units in output layer of SOM


    For the travelling salesman problem of 50 cities, we consider @@ -132,11 +132,9 @@

    Effect of varying the number of units in output layer of SOM

    visited, which indicates the suboptimal nature of the algorithm.

    For 50 cities

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    Figure 2: Illustration of sub-optimal nature of the algorithm
    • From 5e4419b6db7ce7dace88e688e42e3536f97c6dd8 Mon Sep 17 00:00:00 2001 From: Shradha Sehgal Date: Sun, 24 Mar 2019 15:16:46 +0530 Subject: [PATCH 3/4] Made Tutorial image responsive --- src/lab/exp8/Tutorial.html | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/lab/exp8/Tutorial.html b/src/lab/exp8/Tutorial.html index e7a62d1d9..cdec4ad15 100644 --- a/src/lab/exp8/Tutorial.html +++ b/src/lab/exp8/Tutorial.html @@ -127,10 +127,10 @@

    Architecture of SOM

    the region in the output whose neighbourhood units have similar properties.

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    Figure 1: Architecture of SOM
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    Algorithm for learning

    The training of SOM is based on the principle of competitive learning (Refer experiment 7, Artificial Neural Networks virtual labs). From fbc7a5dc7cf35f7f7a152b7b845be5117beac6d1 Mon Sep 17 00:00:00 2001 From: Shradha Sehgal Date: Sun, 24 Mar 2019 15:43:43 +0530 Subject: [PATCH 4/4] Made table & images responsive --- src/lab/exp10/Illustration.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/lab/exp10/Illustration.html b/src/lab/exp10/Illustration.html index 000cd59e8..0022410aa 100644 --- a/src/lab/exp10/Illustration.html +++ b/src/lab/exp10/Illustration.html @@ -105,10 +105,10 @@

    Weighted matching problem


    Example:

    Consider N=4 points as shown in Figure 1 (a). The distances between each pair of points is given in Figure 1 (b).

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    Example:

    Figure 1 (a)

    The possible pairing of points are given in Figures 2 (a) to 2 (c).

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    Figure 2 (a): L=7.2