23April 2019

A Study Guide to Artificial Neural Networks

The Artificial Neural Networks are the computing system that is highly based on the biological neural network. According to the artificial neural network, the structure of the human brain can be imitated by making the exact connections. The silicon and wires can be used as living neurons and dendrites. The Artificial neural network is also known as “manufactured neural framework” and “connectionist systems”.  The human brain is composed of infinity Neurons and Axons. The artificial neural network is a framework whose main concern is to obtain the similarity of the natural neural system. It is the future of computing.

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These networks are inspired by the biologically networks that are specially designed to realize specific computational task and problems. Artificial neural networks are capable to learn from factual information and data. It is very useful in computer science and it also controls engineering fields. The neural network has also made its way to numerous fields of management such as marketing, finance, manufacturing, production, strategic management, and business policy. These networks are highly interconnected, the parallel computational structure with many relatively easy individual processing features.

Types of Artificial Neural Networks

Feedforward ANN: The Feedforward Neural Network is the most common type. The information flows unidirectionally in this network which means a unit sends to the other unit from which it does not receive any information. These networks can be constructed with numerous types of units. It does not have any feedback cycle/loops. It is capable of mapping input patterns to the output patterns.

FeedBack Artificial Neural Network: These are also known as a recurrent neural network. It has feedback loops which are used for content addressable memories.

The Uses of Artificial Neural Networks

  • The Neural Networks are used to make an accurate prediction of future events
  • The characteristics of training data are used to classify unseen data.
  • It clusters the training data into the natural groups.

The ability to learn is the key feature of the artificial neural network. It uses the information flow to change its internal structure.

Several Learning Stages of Neural Networks

Supervised Learning: In this type of learning there is the scholar than ANN itself that feeds the examples of data.

Unsupervised Learning: There is no example data set, therefore, it divides a set of two elements into the group in some unknown pattern.

Reinforcement Learning: The observation is the key element built strategy in this.

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