Neural networks functionality is based on the neuron. It is a cluster of nodes simple components and units. The main scope for researchers in this area is projects on music classification and face recognition. Through the interlinking of simple elements Neural networks projects have performance advantage in cluster node components of units.

Structure of Neural Network:

A cluster of network nodes interlinked with each other with directed links. Every node has a unique activation level.

To activate this activation level each node must abide by set of ethics.

Modelling of Neural Networks Projects:

  • Elementary perception.
  • Linear networks.
  • Network based on logical neurons.

Linear Network: Early neural network model which works on associative memories.

Elementary Perception: Set of input patterns are classified with this. To get this perception a theorem called perception convergence is needed.

Networks Based on Logical Neurons:

Binary state device with operation such as on and off is called logical neuron. In this output and input device should be manually activated.

Properties of Neural Network:

Nonlinearity: Connection of non linear neuron plays an important role in neural network.

Fault Tolerance: There may be occurrence fault during the performance of neural network due to operating conditions.

Input- Output Mapping: Input and output mapping is constructed to provide non parametric static interference.

Contextual Information: Due to the global activity neurons affect each other.

Adaptivity: Synaptic weights of neural network are adaptable to change of the environment.

Learning Process in Neural Networks Projects:

Many researchers used learning to improve neural network. There are three groups of neural network in learning process they are supervised learning, unsupervised learning and reinforcement learning.

Supervised Learning:

With supervision adjustment of weight can be done. Supervised learning primarily does error correction. The difference of desired output and actual output is called as error. If corresponding guidance can only give correct response error occurs.

Unsupervised Learning:

It is a self organized learning. No sign of correct response is shown. Specific regulation of output and input patterns are learnt through unsupervised learning. Adaptive resonance theory and Hebbian rules are made use.

Reinforcement Learning:

It can evaluate the output response by received feedback. New environment is made available by this for new knowledge. It receives input from environment.

Advantages of Neural Networks Projects:

  • Ability to recognize degraded environment.
  • Resistance to noise.
  • Supports parallel processing.
  • Tolerance to distorted images.

Future Scope:

Extracting knowledge from neural network is the future in neural networking. So many researchers are involved in this area. Many new algorithms and rules can be formed to fulfill this process using matlab in neural networks projects.