By Paul D. McNelis (Auth.)
This e-book explores the intuitive charm of neural networks and the genetic set of rules in finance. It demonstrates how neural networks utilized in blend with evolutionary computation outperform classical econometric tools for accuracy in forecasting, category and dimensionality aid. McNelis makes use of various examples, from forecasting motor vehicle creation and company bond unfold, to inflation Read more...
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8 Networks with Multiple Outputs Of course, a feedforward network (or Elman network) can have multiple outputs. 9 shows one such feedforward network architecture, with three inputs, two neurons, and two outputs. 4 What Is A Neural Network? 9. Feedforward network multiple outputs We see in this system that the addition of one additional output in the feedforward network requires additional (k ∗ + 1) parameters, equal to the number of neurons on the hidden layer plus an additional constant term. Thus, adding more output variables to be predicted by the network requires additional parameters which depend on the number of neurons in the hidden layer, not on the number of input variables.
Defining the net set of characteristics, xi , we calculate the value: βxi . If this value is closer to βX 1 than to βX 2 , then we classify xi as belonging to the low-risk group X1 . Otherwise, it is classified as being a member of X2 . 12 However, it is a simple linear method, and does not take into account any assumptions about the distribution of the dependent variable used in the classification. It classifies a set of characteristics X as belonging to group 1 or 2 simply by a distance measure. For this reason it has been replaced by the more commonly used logistic regression.
2]. 27) k=1 where L(nk,t ) represents the logsigmoid activation function with the form 1 . In this system there are i∗ input variables {x}, and k ∗ neu1+e−nk,t rons. A linear combination of these input variables observed at time t, {xi,t }, i = 1, . . , i∗ , with the coefficient vector or set of input weights ωk,i , i = 1, . . , i∗ , as well as the constant term, ωk,0 , form the variable nk,t. This variable is squashed by the logistic function, and becomes a neuron Nk,t at time or observation t.