Fuzzy logic and neural networks

which of the following is not a network architecture

• Single-Layer Feed-Forward Network

• Multi-Layer Feed-Forward Network

• Single Node Without Feedback network

• Multi-Layer Recurrent Network

Which of the following is not a categories in learning of ANN

• Enforcement learning

• supervised learning,

• unsupervised learning,

•  reinforcement learning

which of the following is not an activation function of NN

• Sigmoidal Function

• Hyperbolic Tangent Function (Tanh)

• Triangular Function

• Rectified Linear Unit (ReLU) Function

Which of the following is a learning rule in NN

• Hebeen learning rule.

• Perception learning rule.

• Start Delta learning rule.

• Competitive learning rule.

The soft computing exploits the tolerance of:

• Imprecision

• Uncertainty

• Partial truth

• Analytical model

What is the function of soma in BNN

• responsible for receiving the information from other neurons it is connected to.

• responsible for processing of information.

• It is just like a cable through which neurons send the information.

• It is the connection between the axon and other neuron dendrites

what is the BNN equivalent to ANN output?

• Soma

• Dendrite

• Synapses

• Axon

what is the ANN equivalent to BNN synapses?

• Node

• Input

• Weights

• Output 

What are the Advantages of Neural Networks?

• It can be performed without any problem

• It can be implemented in any application.

• A neural network learns and reprogramming is not necessary

• All of the above

What is back propagation?

• it is another name given to the curvy function in the perceptron

• it is the transmission of error back through the network to adjust the inputs

• it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

• none of the mentioned