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From: Givemore on 14 Jul 2010 01:00 Does anyone know how to formulate an objective function from Nueral Network simulation results to input in the GA for optimisation. I have a trained network which can simulate an ouput based on three inputs (x1, x2, x3). X1 ranges from 0.1:0.6, x2ranges from 5:15, x3 from 20:100. I want to use the trained network to define an objective function and then use GA to find the optimum parameter combination. I am stuck on defining the objective function and would appreciate any help.
From: Alan Weiss on 14 Jul 2010 07:48 On 7/14/2010 1:00 AM, Givemore wrote: > Does anyone know how to formulate an objective function from Nueral > Network simulation results to input in the GA for optimisation. I have a > trained network which can simulate an ouput based on three inputs (x1, > x2, x3). X1 ranges from 0.1:0.6, x2ranges from 5:15, x3 from 20:100. I > want to use the trained network to define an objective function and then > use GA to find the optimum parameter combination. I am stuck on defining > the objective function and would appreciate any help. Take a look at how objective functions are documented: http://www.mathworks.com/access/helpdesk/help/toolbox/gads/brdvu8r.html The main point: put your three inputs into one vector x = [x1 x2 x3] Use x as the input to your fitness function. More examples: http://www.mathworks.com/access/helpdesk/help/toolbox/gads/exampleindex.html Alan Weiss MATLAB mathematical toolbox documentation
From: Greg Heath on 14 Jul 2010 07:49 On Jul 14, 1:00 am, "Givemore " <give...(a)gmail.com> wrote: > Does anyone know how to formulate an objective function from Nueral Network simulation results to input in the GA for optimisation. I have a trained network which can simulate an ouput based on three inputs (x1, x2, x3). X1 ranges from 0.1:0.6, x2ranges from 5:15, x3 from 20:100. I want to use the trained network to define an objective function and then use GA to find the optimum parameter combination. I am stuck on defining the objective function and would appreciate any help. On the few GA nets that I designed, the R^2 statistic was adequate; For training target t and naive constant (i.e., independent of input x) output y00, the mean-square error is obtained from y00 = repmat(mean(t,2),1,N); MSE00 = mse(t-y00) For training target t, input x, and output y, the mean-square error and corresponding R^2 statistic is obtained from y = sim(net,x); MSE = mse(t-y) R2 = 1-MSE/MSE00 Hope this helps. Greg
From: Givemore on 17 Jul 2010 02:37 Greg Heath <heath(a)alumni.brown.edu> wrote in message <ded9b354-8cc4-4231-aa62-ad909f849670(a)r27g2000yqb.googlegroups.com>... > On Jul 14, 1:00 am, "Givemore " <give...(a)gmail.com> wrote: > > Does anyone know how to formulate an objective function from Nueral Network simulation results to input in the GA for optimisation. I have a trained network which can simulate an ouput based on three inputs (x1, x2, x3). X1 ranges from 0.1:0.6, x2ranges from 5:15, x3 from 20:100. I want to use the trained network to define an objective function and then use GA to find the optimum parameter combination. I am stuck on defining the objective function and would appreciate any help. > > On the few GA nets that I designed, the R^2 statistic > was adequate; > > For training target t and naive constant > (i.e., independent of input x) output y00, > the mean-square error is obtained from > > y00 = repmat(mean(t,2),1,N); > MSE00 = mse(t-y00) > > For training target t, input x, and output y, > the mean-square error and corresponding R^2 > statistic is obtained from > > y = sim(net,x); > MSE = mse(t-y) > R2 = 1-MSE/MSE00 > > Hope this helps. > > Greg Hi Greg, Thanks for the input, I am however looking at optimising a process with the variables as listed above. The suggestion you gave will be good for optimising the NN model, I think. Givemore
From: Greg Heath on 18 Jul 2010 12:16
On Jul 17, 2:37 am, "Givemore " <give...(a)gmail.com> wrote: > Greg Heath <he...(a)alumni.brown.edu> wrote in message <ded9b354-8cc4-4231-aa62-ad909f849...(a)r27g2000yqb.googlegroups.com>... > > On Jul 14, 1:00 am, "Givemore " <give...(a)gmail.com> wrote: > > > Does anyone know how to formulate an objective function from Nueral Network simulation results to input in the GA for optimisation. I have a trained network which can simulate an ouput based on three inputs (x1, x2, x3). X1 ranges from 0.1:0.6, x2ranges from 5:15, x3 from 20:100. I want to use the trained network to define an objective function and then use GA to find the optimum parameter combination. I am stuck on defining the objective function and would appreciate any help. > > > On the few GA nets that I designed, the R^2 statistic > > was adequate; > > > For training target t and naive constant > > (i.e., independent of input x) output y00, > > the mean-square error is obtained from > > > y00 = repmat(mean(t,2),1,N); > > MSE00 = mse(t-y00) > > > For training target t, input x, and output y, > > the mean-square error and corresponding R^2 > > statistic is obtained from > > > y = sim(net,x); > > MSE = mse(t-y) > > R2 = 1-MSE/MSE00 > > > Hope this helps. > > > Greg > > Hi Greg, > Thanks for the input, I am however looking at optimising a process with the variables as listed above. The suggestion you gave will be good for optimising the NN model, I think. > Givemore I am not sure I understand you correctly: You want to add input variable subset selection to the weight optimization?. If so, it seems that there should be a combined objective function that includes a penalty for having more and larger weights. Search the FAQ and archives of comp.ai.neural-nets with "weight elimination" Hope this helps. Greg P.S. Make sure you standardize your inputs help zscore |