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Re: Improving ANN results
Posted:
Nov 6, 2013 1:16 AM


"Greg Heath" <heath@alumni.brown.edu> wrote in message <l5cm8f$ak9$1@newscl01ah.mathworks.com>... > "Greg Heath" <heath@alumni.brown.edu> wrote in message <l56r3v$1c9$1@newscl01ah.mathworks.com>... > > "chaudhry " <bilal_zafar9@yahoo.com> wrote in message <l55259$jmo$1@newscl01ah.mathworks.com>... > > > "Greg Heath" <heath@alumni.brown.edu> wrote in message <l4f7fl$dv5$1@newscl01ah.mathworks.com>... > > > > "chaudhry " <bilal_zafar9@yahoo.com> wrote in message <l4a24h$33$1@newscl01ah.mathworks.com>... > > > GREG > > > > > > chk ur mail....i have send u my excel database..... > > > > > > and i have snt u my code also.. > > > so see whats the problem in it and how it can solved > > > > > > because my mse value coming is very large > > > > > > give me solution to my problem kindly as early as possible > > > > Unable to open. Send *.txt or *.m > > Thanks. > > 1. Using minmax I found that x1 is constant. Why in the world would you send me data with a constant input??? > > 2. Removing the constant input, the correlation coefficient matrix for [x ; t ]' indicates > that only x2 is significantly linearly correlated with t. > > % minmaxxt = 8600 11666 > % 76 105000 > % 841 334960 > % > % CC = 1.0000 0.0612 0.1399 > % 0.0612 1.0000 0.7929 > % 0.1399 0.7929 1.0000 > % > % P = 1.0000 0.5924 0.2189 > % 0.5924 1.0000 0.0000 > % 0.2189 0.0000 1.0000 > % > % SIGMASK = 0 0 0 > % 0 0 1 ==> only x2 and t > % 0 1 0 > > 3. Plotting x1 , x2, t, t vs x1 and t vs x2 indicate that there is very little hope of getting > a good nonlinear model from this data unless outliers are removed. > > 4. Standardize the data using zscore or mapstd. Remove outliers and start over.
I used the term 'outlier' because those points are nothing like the others. They may be valid measurements. However, they cannot contribute to a sensible model unless the gap is filled with more data.
Hope this helps.
Greg



