Recent questions tagged neural-network

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1

UGCNET-Jan2017-III: 72
A neuron with $3$ inputs has the weight vector $\begin{bmatrix} 0.2 & -0.1 & 0.1 \end{bmatrix}^{T}$ and a bias $\theta =0.$ If the input vector is $X=\begin{bmatrix} 0.2 & 0.4 & 0.2 \end{bmatrix}^{T}$ then the total input to the neuron is: $0.20$ $1.0$ $0.02$ $-1.0$

A neuron with $3$ inputs has the weight vector $\begin{bmatrix} 0.2 & -0.1 & 0.1 \end{bmatrix}^{T}$ and a bias $\theta =0.$ If the input vector is $X=\begin{bmatrix} 0.2 & 0.4 & 0.2 \end{bmatrix}^{T}$ then the total input to the neuron is: $0.20$ $1.0$ $0.02$ $-1.0$

asked Mar 24, 2020 in Artificial Intelligence jothee 165 views

2 votes

1 answer

2

UGCNET-June-2019-II: 98
Which of the following is an example of unsupervised neural network? Back-propagation network Hebb network Associative memory network Self-organizing feature map

Which of the following is an example of unsupervised neural network? Back-propagation network Hebb network Associative memory network Self-organizing feature map

asked Jul 2, 2019 in Artificial Intelligence Arjun 730 views

2 votes

2 answers

3

ISRO2018-75
ln neural network, the network capacity is defined as: The traffic (tarry capacity of the network The total number of nodes in the network The number of patterns that can be stored and recalled in a network None of the above

ln neural network, the network capacity is defined as: The traffic (tarry capacity of the network The total number of nodes in the network The number of patterns that can be stored and recalled in a network None of the above

asked Apr 22, 2018 in Others Arjun 1k views

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1 answer

4

Uttrakhand Asst. Professor Exam-24
Which of the following can be an application of neural network ? Sales forecasting Data validation Risk management All of the above

Which of the following can be an application of neural network ? Sales forecasting Data validation Risk management All of the above

asked Mar 2, 2018 in Unknown Category gatecse 93 views

1 vote

1 answer

5

UGC NET JULY 2016
A perceptron has input weights W1 = -3.9 and W2 = 1.1 with threshold value T = 0.3. What output does it give for the input x1 = 1.3 and x2 = 2.2? (A) -2.65 (B) -2.30 (B) 0 (D) 1

A perceptron has input weights W1 = -3.9 and W2 = 1.1 with threshold value T = 0.3. What output does it give for the input x1 = 1.3 and x2 = 2.2? (A) -2.65 (B) -2.30 (B) 0 (D) 1

asked Nov 2, 2017 in Others Naqvi 839 views

0 votes

1 answer

6

UGCNET-dec2008-ii-49
An example of a data mining algorithm which uses squared error score function is : CART algorithm back propagation algorithm a priori algorithm vector space algorithm

An example of a data mining algorithm which uses squared error score function is : CART algorithm back propagation algorithm a priori algorithm vector space algorithm

asked Sep 26, 2017 in Computer Networks rishu_darkshadow 286 views

1 vote

1 answer

7

UGCNET-AUG2016-III: 66
Let R and S be two fuzzy relations defined as and Then, the resulting relation, $T$, which relates elements of universe of $X$ to elements of universe of $Z$ using max-product composition is given by

Let R and S be two fuzzy relations defined as and Then, the resulting relation, $T$, which relates elements of universe of $X$ to elements of universe of $Z$ using max-product composition is given by

asked Oct 4, 2016 in Others makhdoom ghaya 725 views

2 votes

1 answer

8

UGCNET-AUG2016-III: 64
What are the following sequence of steps taken in designing a fuzzy logic machine ? Fuzzification $\rightarrow$ Rule evaluation $\rightarrow$ Defuzzification Fuzzification $\rightarrow$ Defuzzification $\rightarrow$ Rule evaluation ... evaluation $\rightarrow$ Fuzzification $\rightarrow$ Defuzzification Rule evaluation $\rightarrow$ Defuzzification $\rightarrow$ Fuzzification

What are the following sequence of steps taken in designing a fuzzy logic machine ? Fuzzification $\rightarrow$ Rule evaluation $\rightarrow$ Defuzzification Fuzzification $\rightarrow$ Defuzzification $\rightarrow$ Rule evaluation Rule evaluation $\rightarrow$ Fuzzification $\rightarrow$ Defuzzification Rule evaluation $\rightarrow$ Defuzzification $\rightarrow$ Fuzzification

asked Oct 4, 2016 in Others makhdoom ghaya 1.4k views

3 votes

1 answer

9

UGCNET-Dec2014-III: 70
Consider the following statements about a perception : $I$. Feature detector can be any function of the input parameters. $II$. Learning procedure only adjusts the connection weights to the output layer. Identify the correct statement out of the following : $I$ is false and $II$ is ... true and $II$ is false. $ I$ is false and $II$ is true. $I$ is true and $II$ is true.

Consider the following statements about a perception : $I$. Feature detector can be any function of the input parameters. $II$. Learning procedure only adjusts the connection weights to the output layer. Identify the correct statement out of the following : $I$ is false and $II$ is false. $I$ is true and $II$ is false. $I$ is false and $II$ is true. $I$ is true and $II$ is true.

asked Aug 2, 2016 in Data Mining and Warehousing makhdoom ghaya 871 views

4 votes

3 answers

10

UGCNET-June2015-III: 72
Suppose the function y and a fuzzy integer number around - 4 for x are given as $y=(x-3)^2 + 2$. Around - 4 ={(2, 0.3), (3, 0.6), (4, 1), (5, 0.6), (6, 0.3)} respectively. Then f(Around-4) is given by {(2, 0.6), (3, 0.3), (6, 1), (11, 0.3)} {(2, 0.6), (3, 1), (6, 1), (11, 0.3)} {(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)} {(2, 0.6), (3, 0.3), (6, 0.6), (11, 0.3)}

Suppose the function y and a fuzzy integer number around - 4 for x are given as $y=(x-3)^2 + 2$. Around - 4 ={(2, 0.3), (3, 0.6), (4, 1), (5, 0.6), (6, 0.3)} respectively. Then f(Around-4) is given by {(2, 0.6), (3, 0.3), (6, 1), (11, 0.3)} {(2, 0.6), (3, 1), (6, 1), (11, 0.3)} {(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)} {(2, 0.6), (3, 0.3), (6, 0.6), (11, 0.3)}

asked Aug 2, 2016 in Others jothee 3.6k views

4 votes

1 answer

11

UGCNET-June2015-III: 71
Let A and B be two fuzzy integers defined as: A={(1.0.3), (2, 0.6), (3, 1), (4, 0.7), (5, 0.2)} B={(10, 0.5), (11, 1), (12, 0.5)} Using fuzzy arithmetic operation given by $\mu_{A+B^{(Z)}} = \underset{x+y=z}{\oplus} (\mu_A (x) \otimes \mu_B(y))$ $f(A+B)$ is _____ . [Note: ... ), (16, 0.5), (17, 0.2)} {(11, 0.3), (12, 0.5), (13, 0.6), (14, 1), (15, 0.7), (16, 0.5), (17, 0.2)}

Let A and B be two fuzzy integers defined as: A={(1.0.3), (2, 0.6), (3, 1), (4, 0.7), (5, 0.2)} B={(10, 0.5), (11, 1), (12, 0.5)} Using fuzzy arithmetic operation given by $\mu_{A+B^{(Z)}} = \underset{x+y=z}{\oplus} (\mu_A (x) \otimes \mu_B(y))$ $f(A+B)$ ... 15, 1), (16, 0.5), (17, 0.2)} {(11, 0.3), (12, 0.5), (13, 0.6), (14, 1), (15, 0.7), (16, 0.5), (17, 0.2)}

asked Aug 2, 2016 in Others jothee 3.1k views

3 votes

1 answer

12

UGCNET-June2015-III: 70
Consider the two class classification task that consists of the following points: Class $C_1$ : [1 1.5] [1 -1.5] Class $C_2$ : [-2 2.5] [-2 -2.5] The decision boundary between the two classes using single perceptron is given by: $x_1+x_2+1.5=0$ $x_1+x_2-1.5=0$ $x_1+1.5=0$ $x_1-1.5=0$

Consider the two class classification task that consists of the following points: Class $C_1$ : [1 1.5] [1 -1.5] Class $C_2$ : [-2 2.5] [-2 -2.5] The decision boundary between the two classes using single perceptron is given by: $x_1+x_2+1.5=0$ $x_1+x_2-1.5=0$ $x_1+1.5=0$ $x_1-1.5=0$

asked Aug 2, 2016 in Others jothee 2.3k views

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13

UGCNET-Sep2013-III: 28
In a single perceptron, the updation rule of weight vector is given by $w(n+1) = w(n) + \eta [d(n)-y(n)]$ $w(n+1) = w(n) - \eta [d(n)-y(n)]$ $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$ $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$

In a single perceptron, the updation rule of weight vector is given by $w(n+1) = w(n) + \eta [d(n)-y(n)]$ $w(n+1) = w(n) - \eta [d(n)-y(n)]$ $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$ $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$

asked Jul 24, 2016 in Machine Learning jothee 1.5k views

1 vote

1 answer

14

UGCNET-Sep2013-III: 27
Support of a fuzzy set $A= \big\{ \frac{x_1}{0.2}, \frac{x_2}{0.15}, \frac{x_3}{0.9}, \frac{x_4}{0.95}, \frac{x_5}{0.15} \big \}$ ... $\{x_3, x_4 \}$ $\{x_1, x_2, x_3, x_4, x_5\}$

Support of a fuzzy set $A= \big\{ \frac{x_1}{0.2}, \frac{x_2}{0.15}, \frac{x_3}{0.9}, \frac{x_4}{0.95}, \frac{x_5}{0.15} \big \}$ ... $\{x_3, x_4 \}$ $\{x_1, x_2, x_3, x_4, x_5\}$

asked Jul 24, 2016 in Others jothee 1.8k views

0 votes

1 answer

15

UGCNET-Sep2013-III: 26
Let A be a set of comfortable houses given as $A = \big\{ \frac{x_1}{0.8}, \frac{x_2}{0.9}, \frac{x_3}{0.1}, \frac{x_4}{0.7} \big \}$ and be the set of affordable houses $B = \big\{ \frac{x_1}{0.9}, \frac{x_2}{0.8}, \frac{x_3}{0.6}, \frac{x_4}{0.2} \big \}$ ... $\big\{ \frac{x_1}{0.7}, \frac{x_2}{0.7}, \frac{x_3}{0.7}, \frac{x_4}{0.9} \big \}$

Let A be a set of comfortable houses given as $A = \big\{ \frac{x_1}{0.8}, \frac{x_2}{0.9}, \frac{x_3}{0.1}, \frac{x_4}{0.7} \big \}$ and be the set of affordable houses $B = \big\{ \frac{x_1}{0.9}, \frac{x_2}{0.8}, \frac{x_3}{0.6}, \frac{x_4}{0.2} \big \}$ Then the set of ... $\big\{ \frac{x_1}{0.7}, \frac{x_2}{0.7}, \frac{x_3}{0.7}, \frac{x_4}{0.9} \big \}$

asked Jul 24, 2016 in Others jothee 1.6k views

1 vote

1 answer

16

UGCNET-June2013-III: 75
Consider a single perception with weights as given in the following figure: and $f(t)$ is defined as $f(t) \bigg\{ 1, t>0 \: 0, t \leq 0$ The above perception can solve OR problem AND problem XOR problem All of the above

Consider a single perception with weights as given in the following figure: and $f(t)$ is defined as $f(t) \bigg\{ 1, t>0 \: 0, t \leq 0$ The above perception can solve OR problem AND problem XOR problem All of the above

asked Jul 19, 2016 in Others jothee 1.9k views

1 vote

1 answer

17

UGCNET-June2013-III: 74
If A and B are two fuzzy sets with membership functions $\mu _A (x)=\{0.6,0.5,0.1,0.7,0.8\}$ $\mu_B(x)=\{0.9, 0.2, 0.6, 0.8, 0.5\}$ Then the value of $\mu_{\overline{A \cup B} }(x)$ will be $\{0.9, 0.5, 0.6, 0.8, 0.8\}$ $\{0.6, 0.2, 0.1, 0.7, 0.5 \}$ $\{0.1, 0.5, 0.4, 0.2, 0.2\}$ $\{0.1, 0.5, 0.4, 0.2, 0.3 \}$

If A and B are two fuzzy sets with membership functions $\mu _A (x)=\{0.6,0.5,0.1,0.7,0.8\}$ $\mu_B(x)=\{0.9, 0.2, 0.6, 0.8, 0.5\}$ Then the value of $\mu_{\overline{A \cup B} }(x)$ will be $\{0.9, 0.5, 0.6, 0.8, 0.8\}$ $\{0.6, 0.2, 0.1, 0.7, 0.5 \}$ $\{0.1, 0.5, 0.4, 0.2, 0.2\}$ $\{0.1, 0.5, 0.4, 0.2, 0.3 \}$

asked Jul 19, 2016 in Others jothee 6k views

2 votes

1 answer

18

UGCNET-June2013-III: 73
A fuzzy set A on R is ______ iff $A(\lambda x_1 + (1- \lambda)x_2) \geq min [A(x_1), A(x_2)]$ for all $x_1, x_2 \in R$ and all $\lambda \in [0,1]$ where minimum denotes the minimum operator. Support $\alpha$ - cut Convex Concave

A fuzzy set A on R is ______ iff $A(\lambda x_1 + (1- \lambda)x_2) \geq min [A(x_1), A(x_2)]$ for all $x_1, x_2 \in R$ and all $\lambda \in [0,1]$ where minimum denotes the minimum operator. Support $\alpha$ - cut Convex Concave

asked Jul 19, 2016 in Others jothee 1.3k views

0 votes

1 answer

19

UGCNET-Dec2012-III: 9
You are given an OR problem and XOR problem to solve. Then, which one of the following statements is true? Both OR and XOR problems can be solved using single layer perception OR can be solved using single layer perception and XOR problem ... using single layer perception OR can be solved using single layer perception and XOR problem can be solved using radial basis function

You are given an OR problem and XOR problem to solve. Then, which one of the following statements is true? Both OR and XOR problems can be solved using single layer perception OR can be solved using single layer perception and XOR problem can be ... be solved using single layer perception OR can be solved using single layer perception and XOR problem can be solved using radial basis function

asked Jul 12, 2016 in Artificial Intelligence jothee 1.6k views

2 votes

3 answers

20

ISRO2011-2
Which of the following is an unsupervised neural network? RBS Hopfield Back propagation Kohonen

Which of the following is an unsupervised neural network? RBS Hopfield Back propagation Kohonen

asked Jun 15, 2016 in Artificial Intelligence Anuanu 2.9k views

tags	tag:apple
author	user:martin
title	title:apple
content	content:apple
exclude	-tag:apple
force match	+apple
views	views:100
score	score:10
answers	answers:2
is accepted	isaccepted:true
is closed	isclosed:true

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