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Neural networks, or more properly Artificial neural networks are computer systems based on a connectionist approach to computation. Simple nodes (or "nerons", or "units") are connected together to form a network of nodes - hence the term "neural network". The orginal inspiration for the technique was from examination of the structures of the brain, and particularly an examination of neurons. |
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Neural networks, or more properly Artificial neural networks are computer systems based on a connectionist approach to computation. Simple nodes (or "neurons", or "units") are connected together to form a network of nodes - hence the term "neural network". The orginal inspiration for the technique was from examination of the structures of the brain, and particularly an examination of neurons. |
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A typical neural network will consist of a set of nodes; some of these are designated input nodes, some output nodes, and those which are neither are referred to as hidden nodes. There will be connections between the neurons, althogh the network is not necessarily dense, with all nodes connected to all other nodes. A weight is associated with each connection. When the network is in operation, values will be applied to the input nodes; these are then passed through weights and a simple computation is performed in each node. These results are then passed to the output nodes. |
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A typical feedforward neural network will consist of a set of nodes; some of these are designated input nodes, some output nodes, and those which are neither are referred to as hidden nodes. There will be connections between the neurons and a weight is associated with each connection. When the network is in operation, values will be applied to the input nodes; these are then passed through weights and a simple computation is performed in each node; taking the sigmoid of the sum of products of the inputs and the weights is typical. These results are then passed through each node in turn until it reaches the output node. |
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One of the truly interesting aspects of how a neural net works, in contrast to some rules-based artificial intelligence approaches, is that a neural network starts out knowing nothing at all. Typically the weights in a neural network are set to random values. |
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Typically the weights in a neural network are set to small random values; this represents the network knowing nothing. As the training process proceeds, these weights will converge to values allowing them to perform a useful computation. Thus it can be said that the neural network commences knowing nothing and moves on to gain some real knowledge. |
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The earliest kind of neural network is a single layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. The sum of the products of the weights and the inputs is calculated in each node, and if the value is above some threshold (typically 0) the neuron fires and takes the value 1; otherwise it takes the value -1. This is accompanied by a learning algorithm which calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights. |
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The earliest kind of neural network is a single layer perceptron network, which consists of a single layer of output nodes; the inputs are fed directly to the outputs via a series of weights. In this way it can be considered the simplest kind of feedforward network. The sum of the products of the weights and the inputs is calculated in each node, and if the value is above some threshold (typically 0) the neuron fires and takes the value 1; otherwise it takes the value -1. This is accompanied by a learning algorithm which calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights. |
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