Abstract: A signal transformation method that transforms an input signal obtained from a subject under a first value of a parameter to an output signal obtainable from said subject under a second value of said parameter is disclosed. The method creates a plurality of neural networks and subjects them to learn the mapping transformation. Genetic programming is used to evolve said plurality of neural networks by applying genetic operators to alter the configurations of said plurality of neural networks. The process of neural learning and genetic altering repeats until a predetermined number of generations is reach. The neural network that performs the mapping transformation best can be selected as the optimal neural network. This optimal neural network can be used subsequently to transform a second input signal to a second output signal for a pre-defined value of the parameter.

Abstract: The present invention is a computer system that analyses the factual and behavioural data of a group of subjects, extracts the common behavioural patterns from the collection, and is capable of forecasting the behaviour of a new subject when his or her factual data are inputted to the system. It does so by first taking a collection of both the factual data and behavioural data from a group of subjects. A clustering engine is employed to compute a set of exemplars that concisely represent the population. Afterwards, the factual data of a subject, and the corresponding behavioural exemplar that he or she belongs, are fed to a learning module so that it can learn the mapping between the subject's factual data and behavioural exemplar. After learning, the system is able to predict the behaviour patterns when factual data of a new subject is presented.