Ensembles of neural networks with different input sets

A computer-based method that generates a trained neural network ensemble, the method comprising:

• receiving a set of available inputs;
• receiving training data comprising values for the available inputs and corresponding values for at least one output;
• training at least one neural network for each of at least two different subsets of the set of available inputs, wherein the at least two different subsets are distinct in that one subset has at least one input signal from a sensor type not included in the other subset;
• providing at least two trained neural networks having different subsets of the available inputs as components of a neural network ensemble that transforms said available inputs into said at least one output, wherein said components are selected at least in part based on a measure of negative correlation between the components;
• and
• plotting said at least one output from the neural network ensemble as a function of at least one of time, depth, and position.

An information storage medium that, when placed in operable relation to a computer, provides the computer with software that generates a trained neural network ensemble, the software comprising:

• a training process that generates a set of neural networks having diversity in inputs and in complexity, wherein diversity in inputs requires that at least one of the neural networks in the set operates on a different combination of input signals than another of the neural networks in the set;
• a selection process that identifies a combination of neural networks from the set having a desirable fitness measure, said fitness measure being based at least in part on a measure of negative correlation for each neural network in the combination;
• a transform process that applies the combination of neural networks in ensemble fashion to a set of inputs to synthesize at least one output;
• and
• a process to display said at least one output.