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Recent questions tagged neural-network

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1
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
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
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
0 votes
1 answer
4
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
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
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
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
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
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
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
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
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
0 votes
1 answer
13
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
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
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
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
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
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
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
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
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