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Friday, March 22, 2013

What is an Artificial Neural Network (ANN)?

- The artificial neural network or ANN (sometimes also called as just neural network) is a mathematical model that has got its inspiration from the biological neural networks. 
- This network is supposed to consist of several artificial neurons that are interconnected. 
- This model works with a connectionist approach for computing and thus processes information based up on this only. 
- In a number of cases, the neural network can act as an adaptive system that has the ability of making changes in its structure while it is in some learning phase. 
- These networks are particularly used in searching patterns in data and for modeling the complex relationships that exist between the outputs and inputs. 
An analogy to artificial neural network is the neuron network of the human brain. 
- In an ANN, the artificial nodes are termed as the neurons or sometimes as neurodes or units or the ‘processing elements’. 
They are interconnected in such a way that they resemble a biological neural network. 
- Till now, no formal definition has been given for the artificial neural networks. - These processing elements or the neurons show a complex global behavior. 
The connections between the neurons and their parameters is what that determines this behavior.
- There are certain algorithms that are designed for altering the strength of these connections in order to produce the desired flow of the signal. 
- The ANN operates up on these algorithms. 
- As in biological neural networks, in ANN also functions are performed in parallel and collectively by the processing units.
- Here, there is no delineation of the tasks that might be assigned to different units. 
- These neural networks are employed in various fields such as:
  1. Statistics
  2. Cognitive psychology
  3. Artificial intelligence
- There are other neural network models that emulate biological CNS and are part of the following:
  1. Computational neuroscience
  2. Theoretical neuroscience
- The modern software implementation of the ANNs prefers a more practical approach than biologically inspired approach. 
- This practical approach is based up on the signal processing and statistics. The former approach has been largely abandoned. 
- Many times parts of these neural networks serve as components for the other larger systems that are a combination of non – adaptive and adaptive elements.
- Even though a more practical approach for solving the real world problems is the latter one, the former has more to do with the connectionist models of the traditional artificial intelligence. 
- Well the common thing between them is the principle of distributed, non – linear, local and parallel processing and adaptation. 
- A paradigm shift was marked by the use of neural networks during the late eighties. 
- This shift was from the high level artificial intelligence (expert systems) to low level machine learning (dynamical system). 
- These models are very simple and define functions such as:
f: X à Y
- Three types of parameters are used for defining an artificial neural network:
a)   The interconnection pattern between neuron layers
b)   The learning process
c)   The activation function
- The second parameter updates the weights of the connections and the third one converts the weighted input in to output. 
- Learning is the thing that has attracted many towards it. 
- There are 3 major learning paradigms that are offered by ANN:
  1. Supervised learning
  2. Un – supervised learning
  3. Reinforcement learning
- Training a network requires selecting from a set of models that would best minimize the cost.
- A number of algorithms are available for training purpose where gradient descent is employed by most of the algorithms.
- Other methods available are simulated annealing, evolutionary methods and so on.

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