Introduction to  CSE Neural Network and Optimization:

Neuro-computing is the advanced computing. It is just like the brain-like computing. It consist of large number of neurons that work simultaneously to solve a particular problem. Here neurons mean processing elements.

In terms of mathematic ANN is an complex non-linear function that adjust the parameters in such a way that ANN output is similar to measured output of an ANN. The first neural network formed was MADALINE, which was applied to the real world.

They are used to extract the pattern ad detect problems that are not possible for humans it solve them. It creates its own organization.  It is used for real time operations that are carried out in parallel. There are some artificial neurons created that are simpler than biological neurons.

Learning process: they are categorized in two general paradigms: associative mapping and regularity detection.  They are classified in two neural networks: fixed networks and adaptive networks.

Applications: it is very widely used in the real world. It used in the robotics systems, for image processing and speech recognition, military services and in health services also. 

They are divided into five categories: prediction, classification, data association, data conceptualization, and data filtering.

Back propagation algorithm: it start with random set of weight and values. One of the set is applied to input layer and then passed to output layers.

Call processing: it takes place when we dial call to any user from our handset or phone. Then a switch is established between origin and destination.

Switching: it determines the path of channel and grants a path to the original telephone we he call to someone.

Traffic control: it uses three simple layer that forward network with propagation algorithm.

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