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Saturday, May 25, 2013

What are advantages and disadvantages of artificial neural networks?

The artificial neural networks, since they can simulate the biological nervous system are used in many real life applications which are also their one of the biggest advantages. 
They have made it easy for carrying out complex processes such as:
Ø  Function approximation
Ø  Regression analysis
Ø  Time series prediction
Ø  Fitness approximation
Ø  Modeling
- With the artificial neuron networks, the classification based on sequence and pattern recognition along with other difficult things such as the sequential decision making and the novelty detection is possible. 
- A number of operations falling under the data processing category such as clustering, filtering, compression and blind source separation etc. are also carried out with the help of artificial neural networks. 
- Artificial neural networks can be considered as the backbone of the robotics engineering field. 
- They are used in computer numerical control and in directing the manipulators.
- It offers advantages in the following fields of:
  1. System control (this including process control, vehicle control and natural resources management),
  2. System identification,
  3. Game – playing,
  4. Quantum chemistry
  5. Decision making (in games such as poker, chess, backgammon and so on.)
  6. Pattern recognition (including face identification, radar systems, object recognition and so on.)
  7. Sequence recognition (in handwritten text recognition, speech, gesture etc.)
  8. Medical diagnosis
  9. Financial applications i.e., in automated trading systems
  10. Data mining
  11. Visualization
  12. E – mail spam filtering
- Today several types of cancers can be diagnosed using the artificial neural networks. 
- HLND is an ANN based hybrid system for detection of lung cancer. 
- The diagnosis carried out with this is more accurate plus the speed of radiology is more. 
- These diagnoses are then used for making some models based up on information of the patient. 
- Its following theoretical properties are nothing but an advantage to the industry:
  1. Computational power: It provides a universal function approximator i.e., the multilayer perceptron or MLP.
  2. Capacity: This property indicates about the ability of ANN to model almost any given function. It has a relation with both the notion of complexity and information contained in a network.
  3. Convergence: This property is dependent on a number of factors such as:
Ø  Number of existing local minima which in turn depends up on model and the cost function.
Ø  Optimization method used
Ø  Impracticality of few methods for a large amount of parameters.
4. Generalization and statistics: Over training is quite a prominent problem in the applications where it is required to create a system that is capable of generalizing in unseen examples. This in turn leads to problem of the over specified or the convoluted systems along with the network exceeding the limit of the parameters. There are two solutions offered by ANN for this problem:
-   Croos – validation and
-   Regularization

Disadvantages of Artificial Neural Networks

1. It requires a lot of diverse training for making the artificial neural networks ready for the real world operations which is a drawback more prominent in the robotics industry.
2. Many storage and processing resources are required for implementing large software neural networks using ANNs.
3. The human has the ability to process the signals via a graph of neurons. A similar simulation of even a very small problem can call for excessive HD and RAM requirements.
4. Time and money cost for building ANNs is very large. 
5. Furthermore, simulation of the signal transmission through all the connections and associated neurons is required.     

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