Neural Network Thesis for Research Scholars.

Neural network is a web of processor and operating system. It gives information on data access. Artificial neural networks are used to develop various applications. An ANN (Artificial Neural Network) can rectify pattern recognition and prediction problems. ANN can also give applications and alternative for classification. Neural network is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. We offer Neural Networks Thesis for Research Scholars with best customer Support.

Architecture of Neural Networks:

Three layer of architecture is derived .

  • Recurrent Network.
  • Single Layer Perceptrons.
  • Multi Layer Perceptrons.

Recurrent Networks: A feedback loop in neural network makes it a recurrent network. It is high in temporal and spatial characters. Models of cognitive functions are made through this behavior of recurrent network. Recurrent network can be divided into two more types:

  • Asymmetric Recurrent System.
  • Symmetric Recurrent System.

Single Layer Perceptrons: Trainable first and simple learning machines are single layer perceptrons. It works under two layer network process. Layer one has fixed connection with fixed function. Layer two gives output with weighted linking methods.


Multilayer Perceptrons: Multilayer forward system contains a set of sensor nodes and units. It also contains input layer, output layer and hidden layer. Multi layer architecture is a supervised function. In this method error detection is done by supervised learning error back propagation.

Artificial Neural Networks Algorithms:

ANN algorithms are divided into two kinds:

  • Self Organization Learning.
  • Associative Learning.

Self Organization Learning:  Self organization learning is otherwise known as unsupervised learning method. It doesn’t have any target value. In the process of clustering network similar patterns can be learnt, distributed and classified into groups.

Associative Learning: Associative learning is also known as supervised learning. Target value or desire output form the base of learning. Network aims to match outputs with desired target values at the time of training process.

Neural Network with Wireless Mesh Network: Internet access for fixed and mobile infrastructure can be obtained through wireless mesh networks. It has periodically changing network topology. To get steady routing in mesh network hybrid routing protocol. This routing is designed by neural network.

Properties of Artificial Neural Networks:

  • Weighted link between elements are available.
  • Representation of knowledge in spread out manner.
  • Simple processing neuron or elements.
  • Learning process is used to collect knowledge.
  • Processing units- nodes Set.

Elements of Artificial Neural Networks

  • Topology-procedure set.
  • Learning Algorithm- Aids to learn patterns.
  • Processing units- nodes set.

Recently neural networks are becoming prominence, as it gives better output for machine communication. It is quite useful in weather forecasting machinery in achieving exact result. ANN is used analog hybrid network in supporting machine to machine network. Thesis writing work was written by the master degree students and PhD research scholars. They propose the new ideas in neural networks.

In wireless sensor network neural network is applied for reduce the energy consumption of the network, in cellular networks for localization and co-channel interference problem neural network is used, neural network can also be used in telecommunications.

Applications of neural network Thesis.
  • Intelligent searching.
  • Fraud detection.
  • Risk management.
  • Process modeling and control.
  • Quality control.
  • Target recognition.
  • Industrial process control.
  • Machine diagnostics.
  • Void recognition.
  • Portfolio management.
  • Credit rating.
  • Financial forecasting.
  • Customer research.
  • Medical diagnosis.
  • Targeted marketing.
  • Categorization.
  • Prediction/ forecasting.
  • Control of data.
  • Pattern classification.
  • Clustering.
  • Function approximation.
  • Optimization.
Back Propagation Neural Network:

Back propagation model is the widely used neural network algorithm. It is used to get complicated output with easy element processing. The back propagation can be done by the below given procedures:

  • Input layer is used to propagate certain input vector.
  • In middle layer vector propagate appears at each node.
  • Output values are calculated by middle layer nodes.
  • For certain input vector output layer is used to compute network.