Neural network development and data analysis tool

An artificial neural network-based data analysis system, comprising:

• an artificial neural network, said neural network comprising a first layer and at least one subsequent layer, each said layer further comprising at least one neuron;
• each said neuron in any of said layers being connected with at least one of said neurons in any subsequent layer, each said connection being associated with a weight value;
• and
• a three-dimensional representation of said artificial neural network.

A neural network trainer, comprising

• an untrained artificial neural network;
• a set of training instructions and parameters for training said untrained artificial neural network;
• and
• a program function operative to convert said trained artificial neural network into a spreadsheet format.

An artificial neural network-based data analysis system, comprising:

• a system algorithm being operative for constructing a proposed, untrained, artificial neural network;
• at least one training file comprising at least one pair of a training input pattern and a corresponding training output pattern and a representation of said training file;
• and
• wherein construction and training of said untrained artificial neural network is initiated by selecting said representation of said training file.

A neural network trainer, comprising:

• at least a first pair of a training input pattern and a corresponding training output pattern;
• a first, untrained, artificial neural network;
• a second, auto-associative artificial neural network, said second artificial neural network being operative to produce a delta value and to calculate a learning rate associated with said first artificial neural network;
• and
• wherein said delta value represents a novelty metric.

An artificial neural network-based data analysis system, comprising:

• at least a first pair of a training input and a corresponding training output;
• a first, untrained, artificial neural network being operative to produce at least one output when at least one input is supplied to said first artificial neural network;
• and
• a comparator portion, said comparator portion being operative to compare an actual output pattern generated by said first artificial neural network as result of said training input pattern being supplied to said first artificial neural network with said corresponding training output, said comparator portion being further operative to produce an output error based on said comparison of said actual output with said corresponding training output and being operative to determine a learning rate and a momentum associated with said first artificial neural network;
• and
• wherein said learning rate and momentum for said first artificial neural network are adjusted in proportion to said output error.

An artificial neural network-based data analysis system, comprising:

• at least a first pair of a training input pattern and a corresponding training output pattern;
• a first, untrained, artificial neural network;
• and
• a first algorithm associated with said system and being operative to generate an architecture, learning rate, and a momentum for said first artificial neural network randomly or systematically;
• at least a second, untrained artificial neural network, said second neural network being trained simultaneously with or sequentially after said first artificial neural network;
• a second architecture, learning rate, and second momentum associated with said second artificial neural network, said architecture, learning rate, and momentum generated randomly or systematically by said first algorithm;
• a comparator algorithm being operative to compare an actual output pattern generated by either of said artificial neural networks as a result of said training input pattern being supplied to either said artificial neural network with said corresponding training output pattern, said comparator algorithm being further operative to produce an output error based on a calculation of a cumulative learning error;
• a third artificial neural network being operative to receive and train on said architectures, learning rates, momentums, and learning errors associated with said first and second artificial neural networks;
• and
• means for varying inputs to said third artificial neural network to observe associated outputs of said third artificial neural network to identify an optimal network architecture and an optimal set of learning parameters.