AI News, Artificial Neural Networks/Neural Network Basics
- On Thursday, October 4, 2018
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Artificial Neural Networks/Neural Network Basics
Artificial Neural Networks, also known as “Artificial neural nets”, “neural nets”, or ANN for short, are a computational tool modeled on the of the neuron in the nervous systems of the human brain and that of other organisms.
Artificial neural networks are very different from biological networks, although many of the concepts and characteristics of biological systems are faithfully reproduced in the artificial systems. Artificial
neural nets are a type of non-linear processing system that is ideally suited for a wide range of tasks, especially tasks where there is no existing algorithm for task completion.
With proper training, ANN are capable of generalization, the ability to recognize similarities among different input patterns, especially patterns that have been corrupted by noise.
The term “Neural Net” refers to both the biological and artificial variants, although typically the term is used to refer to artificial systems only.
Each neuron is a multiple-input, multiple-output (MIMO) system that receives signals from the inputs, produces a resultant signal, and transmits that signal to all outputs.
However, to reproduce the effect of the synapse, the connections between PE are assigned multiplicative weights, which can be calibrated or “trained” to produce the proper system output.
Where ζ is the weighted sum of the inputs (the inner product of the input vector and the tap-weight vector), and σ(ζ) is a function of the weighted sum.
If we recognize that the weight and input elements form vectors w and x, the ζ weighted sum becomes a simple dot product:
The dotted line in the center of the neuron represents the division between the calculation of the input sum using the weight vector, and the calculation of the output value using the activation function.
Neural networks tend to have one input per degree of freedom in the input space, and one output per degree of freedom in the output space.
Expert systems, by contrast, are used in situations where there is insufficient data and theoretical background to create any kind of a reliable problem model.
Expert systems emulate the deduction processes of a human expert, by collecting information and traversing the solution space in a directed manner.
Though such assumptions are not required, it has been found that the addition of such a priori information as the statistical distribution of the input space can help to speed training.
During training, the neural network performs the necessary analytical work, which would require non-trivial effort on the part of the analyst if other methods were to be used.
learning paradigm is supervised, unsupervised or a hybrid of the two, and reflects the method in which training data is presented to the neural network.
A learning rule is a model for the types of methods to be used to train the system, and also a goal for what types of results are to be produced.
During training, care must be taken not to provide too many input examples and different numbers of training examples could produce very different results in the quality and robustness of the network.
Some of the more important parameters in terms of training and network capacity are the number of hidden neurons, the learning rate and the momentum parameter.
These neurons are essentially hidden from view, and their number and organization can typically be treated as a black box to people who are interfacing with the system.
Square root of the sum of squared differences between the network targets and actual outputs divided by number of patterns (only for training by minimum error).
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