Data analysis method and system

摘要

The present invention relates to the analysis of data to identify relationships between the input data and one or more conditions. One method of analyzing such data is by the use of neural networks which are non-linear statistical data modelling tools, the structure of which may be changed based on information that is passed through the network during a training phase. A known problem that affects neural networks is the issue of overtraining which arises in overcomplex or overspecified systems when the capacity of the network significantly exceeds the needed parameters. The present invention provides a method of analyzing data using a neural network with a constrained architecture that mitigates the problems associated with the prior art.

主权项

1. A method of determining a relationship between input data and one or more conditions comprising the steps of:
receiving input data categorised into one or more predetermined classes of condition; training an artificial neural network with the input data, the artificial neural network comprising an input layer having one or more input nodes arranged to receive input data; a hidden layer comprising two or more hidden nodes, the nodes of the hidden layer being connected to the one or more nodes of the input layer by connections of adjustable weight; and, an output layer having an output node arranged to output data related to the one or more conditions, the output node being connected to the nodes of the hidden layer by connections of adjustable weight; determining relationships between the input data and the one or more conditions wherein the artificial neural network has a constrained architecture in which (i) the number of hidden nodes within the hidden layer is constrained; and, (ii) the initial weights of the connections between nodes are restricted and wherein the input data comprises data pairs, each data pair being categorised into the one or more conditions and comprising a parameter and associated parameter value, the training and determining steps comprising: (i) selecting a parameter within the input data, training the artificial neural network with corresponding parameter values and recording artificial neural network performance; (ii) repeating for each parameter within the input data; (iii) determining the best performing parameter in the input data; (iv) repeating steps (i) to (iii), each repetition adding one of the remaining parameters to the best performing combination of parameters, until artificial neural network performance is not improved.