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.