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Unformatted text preview: he constraints and the quantity to be
maximised or minimised.
• Human memory operates in an associative manner. If it hears only a
portion of a known song, it can then produce the rest of the song. A
recurrent net operates in the same manner. If an input vector has, for
example, half of the data and is presented to the net, the net is sometimes
capable of producing the correct output vector. • The bidirectional associative memory (BAM) is heteroassociative; that
is, it accepts an input vector on one set of neurons and produces a related,
but different, output vector on another set. The BAM produces correct 62 outputs despite corrupted inputs. Also, adaptive versions can be abstract,
extracting the ideal from a set of noisy examples.
• The adaptive resonance theory (ART) has the ability to learn new
patterns while preventing the modification of patterns that were learned
previously. The mathematics behind ART are complicated and many people have found the theory difficult to understand. In practice, only one or two hidden layers are used in feedforward multilayer
perceptron nets. The net can be trained by starting with both hidden layers. If
one of the layers is removed, for example to look at the viability of reducing
the sensitivity of the net, the process is called layer pruning.
The number of neurons to be used in hidden layers is not known in advance.
One possible approach is to construct a neural net with an excessive number
of neurons in each hidden layer. When, during the training process, two neurons in the same hidden layer convey the same information, one of the
two neurons must be removed. Neurons in the hidden layer(s) whose outputs
are approximately constant for all training examples must also be removed.
This process of removing neurons is called neuron pruning.
• Generalisation is the ability of the net to be insensitive to variations in the
input data during training and to recognise the pattern in the data despite
noise and distortion. • O vertraining is almost the opposite of generalisation. If the net is overtrained, then small variations in the input data can result in the net’s output
varying significantly from the actual value. For the substation load forecasts, most of the models were overtrained and
again no further attempts were made to continue with neural networks. [42] 63 3.7 CONCLUSION The electrical load model defines the relationship between the key load points
of the transmission load forecast. The different forecasts successfully integrate the different factors and now the results can be checked for
consensus (balance by the balancing algorithm), (See Chapter 4).
3.8 REFERENCE
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2) C.W. Gellings , Demand Forecasting In The Electric Utility Industry,
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4) Makridakis, Wheelwright and Mc Gee , Forecasting Methods and
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