Problem: Design a multilayer perceptron for function approximation y=f(x) based on a given training set.
Network structure: 2 input neurons: x and 1 (bias), one hidden layer of m neurons, one output neuron.
Activation function of hidden neurons :
g (h ) tanh( h )
Activation function of output neuron: linear.
exp( h ) exp( h ) exp( h ) exp( h )
Data sets pairs {x,y}:
- approx_<x>[login to view URL], - training set: 100 examples {x,y}uniformly distributed in the interval (0, 15) (first row – x coords, second row – y values)
- approx_<x>_test.asc. - test set: 400 examples {x,y} uniformly distributed in the interval (0, 15).
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We are a team of 3 Data Scientists based out of Singapore, who have double mathematics and analytics degree with great experience in advanced analytics and statistics. We are also the ONLY partner of Rapidminer in Singapore, which is the leader in Gartner Magic Quadrant for Advanced Analytics and Text Mining Platform. And working languages like R, Python, Java and SAS.
Hi. I can do your task in MATLAB, without using the inbuilt NN models. I'll design as per your requirement. As you haven't mentioned anything about the platform, this is my suggestion.
You can see a lot of projects in my profile, as well as, others in MATLAB section of my blog at www.iquotient.wordpress.com.
Message me whenever free. Thanks.