SEMINARLab

Ultimate way of free seminar, assignment, question&answer, theses, news, downloads....

Neural Networks

Introduction
The term neural network had been used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. In engineering, neural networks serve two important functions: as pattern classifiers and as nonlinear adaptive filters. Thus the term has two distinct usages:

• Biological neural networks: These are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
  
• Artificial neural networks : It is made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks (ANN) are among the newest signal-processing technologies in the engineer's toolbox. The field is highly interdisciplinary, but our approach will restrict the view to the engineering perspective.  The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks. At present, artificial neural networks are emerging as the technology of choice for many applications, such as pattern recognition, prediction, system identification, and control.

An artificial neural network is a system based on the operation of biological neural networks.It is also defined as an emulation of biological neural system. The necessity of the implementation of artificial neural networks is that the computing these days is truly advanced. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots. Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimization and control theory. There are certain tasks that a program made for a common microprocessor is unable to perform; even so a software implementation of a neural network can be made with their advantages and disadvantages.

Advantages:

1. A neural network can perform tasks that a linear program can not.
2. When an element of the neural network fails, it can continue without any problem by their parallel nature.
3. A neural network learns and does not need to be reprogrammed.
4. It can be implemented in any application.
5. It can be implemented without any problem.

Disadvantages:

1. The neural network needs training to operate.
2. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
3. Requires high processing time for large neural networks.

Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes.

The two distinct usages of term:
• Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.
• Artificial neural networks are made up of interconnecting artificial neurons. That means programming constructs that mimic the properties of biological neurons. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system.

Conclusion
An input is presented to the neural network and a corresponding desired or target response set at the output (when this is the case the training is called supervised ). An error is composed from the difference between the desired response and the system output. This error information is fed back to the system and adjusts the system parameters in a systematic fashion (the learning rule).
Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex.
Artificial intelligence and cognitive modelling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems.
The cognitive modelling field involves the physical or mathematical modelling of the behaviour of neural systems; ranging from the individual neural level (e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism .

More reading:
http://en.wikipedia.org/wiki/Artificial_neural_network http://en.wikipedia.org/wiki/Biological_neural_network