Artificial Networks Projects


Artificial Networks Projects have been framed for biological nervous systems. A computational model which values from standard von Neumann architecture is Artificial Neural Network.On ‘learning’ process or training of Artificial Neural Network(ANN) the external environment communicate with the network. Supervised and Unsupervised are the two main types of learning.When the external atmosphere do not support the necessary network output or states it as good or bad is termed as Unsupervised learning.When the network provides correct output for each input pattern weights are already predicted which allows the network to frame out answers nearly related to the correct answers is supervised learning.The part of the weights that are specified under supervised learning is known as Hybrid learning where others are derived through an unsupervised learning. The  classifications of network are feed forward or feedback ANN.In a feed forward ANN it gives the output to units from where it do not get an input directly or indirectly.

 2015 IEEE Artificial Networks Projects

  Characteristics of Artificial Networks Projects:

  • Fault Tolerance.
  • Learning Ability.
  • Adaptivity.
  • Massive Parallelism.
  • Distributed Computation and Representation.
  • Generalization ability.
  • Low Energy Consumption.
  • Inherent Processing of Contextual information.

Artifical Network Projects – Area’s

  • Function Approximation.
  • Optimization.
  • Feature Recognition.
  • Associative memories.

Types of Neural Network Learning in Artificial Networks Projects :

Perception Learning Rule.

Hebbian Learning Rule.

Hebbian learning rule is a general process to calculate changes in connection strengths in a neural network where function of the pre and post synaptic neural activities changes the connection strength.

Steps for modifying the weights and biases of network deals in perception learning rule.Networks output is stated by


Types of Artificial Neural Network Projects:

Adaptive Resonance Theory(ART).

Kohonen nets.

Radical Basis function network.

Mathematical and biological background are present at Kohonen`s model.There are three Different Types in Artificial neural network Projects A data visualization technique which brings down the dimension of data by using self-organizing feature Maps(SOFM).

Adaptive Resonance Theory which is based on artificial neural network form specifies groups of pattern recognition methods is known as stability plasticity dilemma and the main goal is to overcome the less powerful discrimination power. From the theory of function approximation Radial Basis function is derived .

Artifical Network projects paper title are updated every year from the updated every year from the reputed journal as science direct each year.All the above technique artificial networks projects are been done by our concern and we support B.E/M.E/ students for Artificial networks projects.