A framework for the investigation of multiparametric dependences applied to total radiated power of JET plasmas

A framework is developed for investigating complex multivariate relationshi ps in a dataset. This is based on using the universal approximation abiliti es of a multi-layer perceptron (MLP) neural network to predict a quantity o f interest from a large set of parameters. A measure of redundancy is deriv ed, and used in such a way that the average influence on the predicted quan tity from any parameter can be estimated. Input parameters can be ordered i n terms of increasing redundancy and therefore assist in finding the most i mportant parameters a phenomenon of interest depends upon. In spite of the problem being multi-dimensional, the functional form of the one-to-one rela tionship between a parameter and a quantity of interest can be visualized. This framework is then used together with sensitivity analysis to investiga te the dependence of the total radiated power of JET plasmas on a large num ber of parameters, leading to the identification of a much smaller set of p arameters to be used in an effective MLP predictor of total radiated power.