Artificial Neural Networks

• A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.

• An ANN is based on a collection of connected units or nodes called artificial neurons which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.

• The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Types of Artificial Neural Networks:

There are two Artificial Neural Network topologies − FeedForward 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 ANN

Here, feedback loops are allowed. They are used in content addressable memories

Working of ANNs:

In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons.

If the network generates a “good or desired” output, there is no need to adjust the weights. However, if the network generates a “poor or undesired” output or an error, then the system alters the weights in order to improve subsequent results.

Machine Learning in ANNs:

ANNs are capable of learning and they need to be trained. There are several learning strategies −

• Supervised Learning.

• Unsupervised Learning.

• Reinforcement Learning.

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Next week I will post more about AI...so stay tuned!