Artificial Neural Networks Projects

A family of statistical viewing algorithms aspired by biological neural networks which are used to estimate tasks carried on large number of inputs that are generally unknown in Artificial Neural Networks Projects.As systems of interconnected ‘neurons’ to calculate values from input users Artificial Neural Networks that are capable of machine learning and recognizing  the pattern for their adaptive nature. Artificial Neural Networks Projects are supported to PhD scholars. we update from the reputed journals ACM the paper title for Artificial Neural Networks Projects.



Three types of parameters are used to define Artificial Neural Networks Projects:

  • The various layers of neurosis and their interconnection pattern.
  • For updating the interconnection weights and their learning process.
  • Activating function which changes neurons weight input to active output.

 To develop intelligent systems neural advances have been carried out by the aspiration of biological neural networks. TO solve problems in prediction, control, associative memory, optimization and pattern reorganization, learning ability, distributed representation and computation, generalization ability, adaptively, fault tolerance, inherent contextual information processing and low energy consumption.

Pattern classification:

                To assign an input pattern which is represented by a feature vector to one pre specified class is pattern classification.

Function approximation:

                Generating pairs form unknown functions (I.e.) input-output pairs.


Exploring the similarity between places and patterns in a cluster. It is an unsupervised classification of pattern with known class labels

Forecasting /Prediction:

For science, business and engineering has a useful impact.


                Faces huge problems in medicine, science, engineering statistics mathematics and economics can be posed as optimization problems.

Content addressable Memory:

                An entry in memory is accessed in von Neumann model of computation which is independent to the content in the Memory.