CHARACTERISTICS OF NEURAL NETWORK
Basically Computers are good in calculations that takes inputs process then and gives the result as per the calculations
which is done by using the particular Algorithm which are programmed in the software's but ANN uses its own rules,
the more decisions they make, the better decisions may become.
The Characteristics are basically those which should be present in intelligent System like robots and other Artificial Intelligence Applications.
There are six characteristics of Artificial Neural Network which are basic and important for this technology which are showed with the help of diagram:
The Network Structure
The Network Structure of ANN should be simple and easy. There are basically two types of structures recurrent and non recurrent structure.
The Recurrent Structure is also known as Auto associative or Feedback Network and the Non Recurrent Structure is also known as Associative
or feed-forward Network. In Feed forward Network, the signal travel in one way only but in Feedback Network,
the signal travel in both the directions by introducing loops in the network. As shown in the figures below:
Ability of Parallel Processing
ANN is only the concept of parallel processing
in the computer field. Parallel Processing is done
by the human body in human neurons that is
very complex but by applying basic and simple
parallel processing techniques we implement it
in ANN like Matrix and some matrix
calculations.
Distributed Memory
ANN is very vast system so single unit memory
or centralized memory cannot fulfill the need of
ANN system so in this condition we need to
store information in weight matrix which form a
long term memory because information is stored
as patterns throughout the network structure
Fault Tolerance Ability
ANN is a very complex system so it is necessary
that it should be a fault tolerant. Because if any
part becomes fails it will not affect the system as
much but if the all parts fails at the same time
the system will fails completely.
Collective Solution
ANN is a interconnected system the output of a
system is a collective output of various input so the result is summation of all the outputs which
comes after processing various inputs. The
Partial answer is worthless for any user in the
ANN System.
Learning Ability
In ANN most of the learning rules are used to
develop models of processes, while adopting the
network to the changing environment and
discovering useful knowledge. These Learning
methods are Supervised, Unsupervised and
Reinforcement Learning
