Artificial neural networks are relatively crude mathematical based electronic models based on the neural structure of the brain.The brain basically learns experience.
Neural network structures represent models for "thinking" that are still being evolved in the laborateries.All these are simple tools .
Conceptually,ANN are computing constructs which mimic the process of the human brain.Mathematically,they are a system of linked parallel equations which are solved simultaneously and iteratively.
ANN is configured for a specific application,such as pattern recognition or data classification,through a learning process.
They have got many benefits over other kinds of networks
- Adaptive learning
- self organizing
- real time operation
- fault tolerence via redundant information coding
ADVANTAGES OF NEURAL NETWORKS:
1.Parallel Processing
2.Distributing representations
3.Online algorithm
4.simple computations
5.Robust with respect to noisy data
6. Robust with respect to node failure
7.Empirically shown to work well for many problem domains.
DISADVANTAGES OF NEURAL NETWORKS:
1.Slow training
2.Poor interpretability
3.Network topology layouts adhoc
4.Hard to debug.
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