According to a first aspect of the invention there is provided a method of decision-making comprising: a
data input step to input data from a plurality of first data sources into a first
data bank, analysing said input data by means of a first adaptive
artificial neural network (ANN), the neural network including a plurality of
layers having at least an input layer, one or more hidden
layers and an output layer, each layer comprising a plurality of interconnected neurons, the number of hidden neurons utilised being adaptive, the ANN determining the most important input data and defining therefrom a second ANN, deriving from the second ANN a plurality of Type-1 fuzzy sets for each first
data source representing the
data source, combining the Type-1 fuzzy sets to create
Footprint of Uncertainty (FOU) for type-2 fuzzy sets, modelling the group decision of the combined first data sources; inputting data from a second
data source, and assigning an aggregate
score thereto, comparing the assigned aggregate
score with a
fuzzy set representing the group decision, and producing a decision therefrom. A method employing a developed ANN as defined in Claim 1 and extracting data from said ANN, the data used to learn the parameters of a normal
Fuzzy Logic System (FLS).