Artificial Neural Networks (ANN) and Different Types

An Artificial Neural Network (ANN) is modeled on the brain where neurons are connected in complex patterns to process data from the senses, establish memories and control the body. An Artificial Neural Network (ANN) is a system based on the operation of biological neural networks or it is also defined as an emulation of biological neural system.

Artificial Neural Network
Artificial Neural Network

Artificial Neural Networks (ANN) is a part of Artificial Intelligence (AI) and this is the area of computer science which is related in making computers behave more intelligently. Artificial Neural Networks(ANN) process data and exhibit some intelligence and they behaves exhibiting intelligence in such a way like pattern recognition,Learning and generalization.

An artificial neural network is a programmed computational model that aims to replicate the neural structure and functioning of the human brain.

Before knowing about Artificial Neural Networks, at first we need to study what are neural networks and also about Structure of Neuron.

Definition of Neural Networks:

The Neural networks are defined as the systems of interconnected neurons. Neurons or Nerve Cells are the basic building blocks of brains which are the biological neural networks. The structure of Neuron is as show below

Structure of Neuron
Structure of Neuron

Artificial Neural Networks are the computational tools which were modeled after brains. It is made up of an interconnected structure of artificially produced neurons that function as pathways for data transfer. Researchers are designing artificial neural networks (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control.


Artificial neural networks have been described as the second best way to form interconnected neurons. These artificial neural networks are used to model brains and also to perform specific computational tasks.A successful ANN application will have the capability of character recognition.

Neural Network Structure
Neural Network Structure

Introduction to Neural Networks:

A computing system is made up of a number of simple, highly interconnected processing elements and they process information to external inputs with their dynamic state response.A neuron has the ability to produce a linear or a non-linear response. A non-linear artificial network is made by the interconnection of non-linear neurons. Non-linear systems have inputs which will not be proportional to outputs.

Introduction to Neural Networks
Introduction to Neural Networks

Applications of Artificial Neural Networks:

  • Artificial neural network applications have been used in the field of solar energy for modeling and design of a solar steam generating plant.
  • They are useful in system modeling, such as in implementing complex mapping and system identification.
  • ANN are used for the estimation of heating-loads of buildings, parabolic-trough collector’s intercept factor and local concentration ratio
  • ANN are used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences.
  • They have also been used for the prediction of air flows in a naturally ventilated test room and for the prediction of the energy  consumption of solar buildings.
  • They are able to handle noisy and incomplete data and also able to deal with non-linear problems
  • The use of artificial neural-networks in ventilating and air-conditioning systems, refrigeration,modeling, heating, load-forecasting, control of power-generation systems and solar radiation.

An Artificial Neural Network Application provides an alternative way to tackle complex problems as they are among the newest signal processing technologies.Artificial neural networks offer real solutions which are difficult to match with other technologies. Neural network based solution is very efficient in terms of development, time and resources.

Software implementation of a neural network can be made with their advantages and disadvantages.


  • A neural network can perform tasks in which a linear program cannot perform.
  • When an element of the neural network fails, it can continue without any problem by their parallel nature.
  • A neural network does not need to be reprogrammed as it learns itself.
  • It can be implemented in an easy way without any problem.
  • As adaptive, intelligent systems, neural networks are robust and excel at solving complex problems. Neural networks are efficient in their programming and the scientists agree that the advantages of using ANNs outweigh the risks.
  • It can be implemented in any application.


An Artificial Neural Network is developed with a systematic step-by-step procedure which optimizes a criterion commonly known as the learning rule. The input/output training data is fundamental for these networks as it communicates the information that will be necessary to discover the optimal operating point. A non-linear nature of neural network makes its processing elements flexible in their system.

An artificial neural network is a system and this system is a structure which receives an input, processes the data and provides an output. The input in data array will be WAVE sound, a data from an image file or any kind of data that can be represented in an array. Once an input is presented to the neural network required target response is set at the output and from the difference of the desired response along with the output of real system an error is obtained. The error information is fed back to the system and it makes many adjustments to their parameters in a systematic order which is commonly known as the learning rule. This process is repeated until the desired output is accepted.

It is observed that the performance hinges heavily on the data, so the data should be pre-processed with third party algorithms such as DSP algorithms.

Benefits of Artificial Neural Networks:

  • Artificial neural networks are flexible and adaptive.
  • Artificial neural networks are used in sequence and pattern recognition systems, data processing, robotics, modeling, etc.
  • ANN acquires knowledge from their surroundings by adapting to internal and external parameters and they solve complex problems which are difficult to manage.
  • It generalizes knowledge to produce adequate responses to unknown situations.
  • Flexibility – Artificial neural networksare flexible and have the ability to learn, generalize and adapts to situations based on its findings.
  • Non-Linearity – This function allows the network to efficiently acquire knowledge by learning. This is a distinct advantage over a traditionally linear network that is inadequate when it comes to modelling non-linear data.
  • An artificial neuron network is capable of greater fault tolerance than a traditional network. Without the loss of stored data, the network is able to regenerate a fault in any of its components.
  • An artificial neuron network is based on Adaptive Learning.

Types of Artificial Neural Networks:

There are different types of Artificial Neural Networks (ANN)– Depending upon the human brain neuron and network functions, an artificial neural network or ANN performs tasks in a similar manner. Most of the artificial neural networks will have some resemblance with more complex biological counterparts and are very effective at their intended tasks like for e.g. segmentation or classification. Types of Artificial Neural Networks

Types of Artificial Neural Networks
Types of Artificial Neural Networks

Feedback ANN – In these type of ANN, the output goes back into the network to achieve the best-evolved results internally. The feedback network feeds information back into itself and is well suited to solve optimization problems, according to the University of Massachusetts, Lowell Center for Atmospheric Research. Feedback ANNs are used by the Internal system error corrections.

Feed Forward ANN – A feed-forward network is a simple neural network consisting of an input layer, an output layer and one or more layers of neurons.Through evaluation of its output by reviewing its input, the power of the network can be noticed base on group behavior of the connected neurons and the output is decided. The main advantage of this network is that it learns to evaluate and recognize input patterns.

Classification-Prediction ANN –It is the subset of feed-forward ANN and the classification-prediction ANN is applied to data-mining scenarios. The network is trained to identify particular patterns and classify them into specific groups and then further classify them into “novel patterns” which are new to the network.

An artificial neural network is a computational simulation of a biological neural network.These possess the behavior of neurons and the electrical signals in which they communicate between input such as from the eyes or nerve endings in the hand to the output of the brain such as reacting to light, touch or heat.

Scientists were researching in the designing of artificial neural networks and the creation of artificial intelligence about the way neurons semantically communicate.

Neural Network Software:

Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks.They focus on one or a limited number of specific types of neural networks.Neural network simulation often provides faster and more accurate prediction comparing with other data analysis methods as these neural networks play a significant role in data mining process.

Neural network Software
Neural network Software

They are typically stand-alone and do not intend to generate neural networks which has to be integrated in other software. Simulators usually have some form of built-in visualization to monitor the training process. Some simulators also visualize the physical structure of the neural networks. The concept of Neural Network is widely used for analysis of data. With the help of artificial neural network software, time series forecasting, function approximation and regression analysis can be carried out.Scope of neural networks is virtually limitless decision making, pattern recognition, forecasting, automatic control systems and many others.

A neural network does not have the need to be “reprogrammed” once it learns anything similar to human.

Neural Network Simulation
Neural Network Simulation

The main aim and intention behind the development of ANNs is that they explain the artificial computation model with the basic biological neuron.They outline network architectures and learning processes by presenting multi layer feed-forward networks. It is suggested that artificial neural-networks can be used for modelling in other fields of energy production. Why would be necessary the implementation of artificial neural networks? If you have any queries just comment below or visit our site.

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