Write any three terminating conditions used in learning of a neural network.
In the context of neural network training, there are several terminating conditions that are commonly used to determine when the training process should stop. Here are three such conditions:
1. Epoch Limit: This condition _________ ______ ___ _____ ___ ____ _____.
______ ________ _________ _________ ___ ___ _______ __________ ____ _______ _______ _______.
__________ ________ _____ ____ _______ _________ ____ ______ _______ ________ ___.
____ ___ _______ ___ _____ _________ _______ ____ _______ __________ _____ ___.
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___ __________ __________ __________ ___ ________ ________ ______ ____.
_______ ________ ___ ________ _______ ____ _______ _____ ____ ______ _________ _________.
________ _____ ___ ___ _____ ______ __________ ________ _________ ___ ___.
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Consider the two parents which are participating in partially mapped cross over as shown below:
Parent 1: C D | E A B I | H G F
Parent 2: A B | C D E F | G H I
Using partially mapped crossover assuming 2nd and 6th as the crossover sites, find the children solution.
a) The input to a single input neuron is 2, its weight is 2.3 and its bias is - 3 . The neuron output for Linear transfer function is -1 .
b) The SOM is useful for classification.
c) Gradient based optimization methods are used when the objective function is not smooth and one needs efficient local optimization.
d) The -cut of a fuzzy set A in
is defined as A
. {x
A (x)
0 }.
e) If W (k0) = W (k0 +1 (k0 + 2) , then perceptron is non-linear separable.
Describe the Binary Hopfield network with the help of an example.
Differentiate between bounded sum and algebraic sum of two fuzzy sets.
Find max-average composition for R ( x , y ) and S ( x , y ) defined by the following relational matrices:
Write a formula describing the function defined by a one-hidden-layer (already trained) MLP with a single output. Also, write the formula describing the function defined by a RBFN with a single output. How do they differ?
Consider the ADALINE filter with three neurons in the input layer having weights w11 = 3, w12 = -2 and w13 -2 and the input sequence {- - - , 0, 0, 0, -4, 5, 0, 0, 0 - - - - } What is the filter output?