Abstract: In the present invention signals are obtained from a plurality of sensors S1, S2, Sn and fed into an encoder (12). The encoder (12) is operable in use to receive input signals from each of the sensors S1, S2, Sn and translate said signals into one or more vectors characterising the state of one or more of the operational parameters of the monitored system, hereinafter referred to as an encoded vector VE. The signals from the sensors S1, S2, Sn may relate to one or more different operational parameters of a connected system. The encoded vector VE is fed into a translation engine 13 which translates the encoded vector VE into feature space to form a feature vector VF.

Abstract: 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 utilized 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 wit

Abstract: 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 wit