3. What is an ANN? Describe various types of ANN. Which ANN do you prefer amidst of the variety of ANNs? Justify the reason beyond this. Solution What is ANN?: An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. ANNs have three layers that are interconnected. The first layer consists of input neurons. Those neurons send data on to the second layer, which in turn sends the output neurons to the third layer. Types of artificial neural networks: There are two Artificial Neural Network topologies FreeForward and Feedback. FeedForward ANN The information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs. Feedback.: Here, feedback loops are allowed. They are used in content addressable memories. Radial Basis Function (RBF) Neural Network – Radial basis functions are powerful techniques for interpolation in multidimensional space. A RBF is a function which has built into a distance criterion with respect to a center. RBF neural networks have the advantage of not suffering from local minima in the same way as Multi-Layer Perceptrons. RBF neural networks have the disadvantage of requiring good coverage of the input space by radial basis functions. Kohonen Self-organizing Neural Network – The self-organizing map (SOM) performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these. Recurrent Neural Networks – Recurrent neural networks (RNNs) are models with bi-directional data flow. Recurrent neural networks can be used as general sequence processors. Various types of Recurrent neural networks are Fully recurrent network (Hopfield network and Boltzmann machine), Simple recurrent networks, Echo state network, Long short term memory network, Bi- directional RNN, Hierarchical RNN, and Stochastic neural networks. Modular Neural Network : Biological studies have shown that the human brain functions not as a single massive network, but as a collection of small networks. This realization gave birth to the concept of modular neural networks, in which several small networks cooperate or compete to solve problems. Physical Neural Network : A physical neural network includes electrically adjustable resistance material to simulate artificial synapses. Feed Forwad is mostly used ANN Network due to its different applications: 1)Physiological feed-forward system:In physiology, feed-forward control is exemplified by the normal anticipatory regu.

1. 3. What is an ANN? Describe various types of ANN. Which ANN do you prefer amidst of the
variety of ANNs? Justify the reason beyond this.
Solution
What is ANN?:
An artificial neuron network (ANN) is a computational model based on the structure and
functions of biological neural networks. Information that flows through the network affects the
structure of the ANN because a neural network changes - or learns, in a sense - based on that
input and output.
ANNs have three layers that are interconnected. The first layer consists of input neurons. Those
neurons send data on to the second layer, which in turn sends the output neurons to the third
layer.
Types of artificial neural networks:
There are two Artificial Neural Network topologies FreeForward and Feedback.
FeedForward ANN
The information flow is unidirectional. A unit sends information to other unit from which it does
not receive any information. There are no feedback loops. They are used in pattern
generation/recognition/classification. They have fixed inputs and outputs.
Feedback.:
Here, feedback loops are allowed. They are used in content addressable memories.
Radial Basis Function (RBF) Neural Network –
Radial basis functions are powerful techniques for interpolation in multidimensional space. A
RBF is a function which has built into a distance criterion with respect to a center. RBF neural
networks have the advantage of not suffering from local minima in the same way as Multi-Layer
Perceptrons. RBF neural networks have the disadvantage of requiring good coverage of the input
space by radial basis functions.
Kohonen Self-organizing Neural Network – The self-organizing map (SOM) performs a form of
unsupervised learning. A set of artificial neurons learn to map points in an input space to
coordinates in an output space. The input space can have different dimensions and topology from
the output space, and the SOM will attempt to preserve these.
Recurrent Neural Networks – Recurrent neural networks (RNNs) are models with bi-directional
data flow. Recurrent neural networks can be used as general sequence processors. Various types
of Recurrent neural networks are Fully recurrent network (Hopfield network and Boltzmann
machine), Simple recurrent networks, Echo state network, Long short term memory network, Bi-

2. directional RNN, Hierarchical RNN, and Stochastic neural networks.
Modular Neural Network : Biological studies have shown that the human brain functions not as a
single massive network, but as a collection of small networks. This realization gave birth to the
concept of modular neural networks, in which several small networks cooperate or compete to
solve problems.
Physical Neural Network :
A physical neural network includes electrically adjustable resistance material to simulate
artificial synapses.
Feed Forwad is mostly used ANN Network due to its different applications:
1)Physiological feed-forward system:In physiology, feed-forward control is exemplified by the
normal anticipatory regulation of heartbeat in advance of actual physical exertion
2)Feed-forward systems:In computing:In computing, feed-forward normally refers to a
perceptron network in which the outputs from all neurons go to following but not preceding
layers, so there are no feedback loops. The connections are set up during a training phase, which
in effect is when the system is a feedback system.
3)Automation and Machine Control[edit]
Feedforward control is a discipline within the field of automatic controls used in automation.