Download Artificial Higher Order Neural Networks for Economics and by Ming Zhang PDF

By Ming Zhang

Man made greater Order Neural Networks (HONNs) considerably switch the examine technique that's utilized in economics and company parts for nonlinear facts simulation and prediction. With the $64000 advances in HONNs, it turns into crucial to stay familiar with its advantages and enhancements. man made better Order Neural Networks for Economics and company is the 1st ebook to supply useful schooling and purposes for the hundreds of thousands of execs operating in economics, accounting, finance and different company components on HONNs and the convenience in their utilization to acquire extra exact program effects. This resource presents major, informative developments within the topic and introduces the techniques of HONN team types and adaptive HONNs.

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Model 1b has three layers of changeable weights, Model 1 has two layers of changeable weights, and model 0 has one layer of changeable weights. For models 1b, 1 and 0, Z is the output while x and y are the inputs of kj k , j =0 z= HONN strUctUrE AND NONLINEAr MODLEs Model n where : and kj o 0: { f k x ( x)}{ f j y ( y )} (akj hx ) = ( akj hy ) = 1 ak x = a j y = 1 (3) For equations 1, 2, and 3, values of k and j ranges from 0 to n, where n is an integer. The HONN model can simulate any nonlinear function.

Users have less chance to guess correct initial coefficients, since the convergence range is small. comparison Using Us Population Growth Data The US population growth data has a total of 21 inputs from 1790 to 1990. 929 to Equation (42). since f j y (a j y y ) = f j y = [1 / (1 + exp(- net j y )] j = [1 / (1 + exp( - a j y y ))] j and net j y = a j y f j y '(net j y ) = j[1 / (1 + exp( - a j y y )] j -1 * (1 + exp(- a j y y )) -2 * exp( - a j y y )] Then a j y (t + 1) = a j y (t ) - (∂E p / ∂a j y ) = a j y (t ) + (d - z ) f o '(net o )akj o * f h '(netkj h )akj hx bk x akj hy f j y '(net j y ) y = a j y (t ) + * ol * akj o * hy * akj hy * f j y '( net j y ) * y = a j y (t ) + * ol * akj o * hy * akj hy *[ j[1 / (1 + exp(- a j y y )] j -1 * (1 + exp( - a j y y )) -2 * exp(- a j y y )]]* y = a j y (t ) + * ol * akj o * kj hy * akj hy * j y *y where : = (d - z ) ol kj j hy y hx = akj bk x f o '(net o ) = 1 and and h f '(netkj ) = 1 hy (linear (linear neuron) neuron) = f j y '(net j y ) = j[1 / (1 + exp( - a j y y )] j -1 * (1 + exp(- a j y y )) -2 * exp( - a j y y )]  Artificial Higher Order Neural Network Nonlinear Models Table 1 Quadratic with Plateau Data Modeling Accuracy - SAS NLIN or HONNs?

HONN models have better modeling accuracy than SAS NLIN modeling result. 10% better than SAS NLIN model. input* (Year) Desired Output * (Population Million) SAS NLIN Output Error (pop-model. 8868** *: These numbers are published in the SAS NLIN manual. **: These numbers are calculated base on the data in the SAS NLIN manual. ***: HONN better than SAS (%) = (SAS MSE - HONN MSE) /(SAS MSE)*100%  Artificial Higher Order Neural Network Nonlinear Models and HONN. This table lists 4 HONN models that have a smaller residual mean squared error than that of SAS NLIN model.

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