Neural network, neuron, and method for recognizing a missing input valve

A neuron for recognizing a missing input value, comprising:

• a predetermined number of first inputs for receiving inputs x.sub.i of the neuron;
• a plurality of second inputs corresponding to each of said predetermined number of first inputs for receiving null values n.sub.i ;
• each null value n.sub.i being in either a first logic state if a corresponding input x.sub.i is missing, or a second logic state otherwise;
• a plurality of null-inhibiting weighting elements for storing a plurality of weights w.sub.i corresponding to each of said predetermined number of first inputs, each null-inhibiting weighting element providing an output thereof y'.sub.i as a product of a corresponding input x.sub.i, said a corresponding weight w.sub.i, and a corresponding null value n.sub.i ;
• a summing device coupled to said plurality of null-inhibiting weighting elements, for summing outputs of each null-inhibiting weighting element to provide a net output NET defined by NET =.SIGMA.x.sub.i *w.sub.i *n.sub.i ;
• and
• an activation element coupled to said summing device, for providing an output of the neuron in response to said net output NET and a predetermined activation function.

A neural network for recognizing a missing input value comprising:

• a first layer of segmented neurons of a first type receiving a plurality of input signals and a plurality of null inputs corresponding to each of said plurality of input signals, and providing a plurality of first output signals in response to said plurality of input signals and said plurality of null inputs;
• a second layer of neurons of a second type operably coupled to said first layer of segmented neurons, said second layer of neurons receiving each of said plurality of first output signals, and activating at least one of a plurality of second output signals in response;
• and
• a third layer of neurons of a third type operably coupled to said second layer of neurons, said third layer of neurons receiving said plurality of second output signals, and providing at least one output of the neural network in response to said at least one of said plurality of second output signals activated by said second layer of neurons;
• said first and second types characterized as being unsupervised and competitive;
• said third type characterized as being supervised and noncompetitive.

A method for recognizing a missing input value while estimating an unknown parameter, comprising the steps of:

• inputting a plurality of first measured values and a plurality of first null values corresponding thereto to a first neuron segment, each of said plurality of first null values indicating when in an active logic state thereof that a corresponding one of said plurality of first measured values is missing;
• inputting a plurality of second measured values and a plurality of second null values corresponding thereto to a second neuron segment, each of said plurality of second null values indicating when in an active logic state thereof that a corresponding one of said plurality of second measured values is missing;
• operably coupling together each of a plurality of neurons of said first neuron segment such that each of said plurality of neurons of said first neuron segment provides an output thereof in partial dependence on said output of others of said plurality of neurons of said first neuron segment;
• operably coupling together each of a plurality of neurons of said second neuron segment such that each of said plurality of neurons of said second neuron segment provides an output thereof in partial dependence on said output of others of said plurality of neurons of said second neuron segment;
• and
• estimating the unknown parameter from said outputs of each of said first and second neuron segments when at least one of said pluralities of first and second null values is inactive.