Signal phase pattern sensitive neural network system and method

Claim 2

A signal phase sensitive neural network system, comprising: (a) a plurality of means for sensing a signal emanating from an environment of interest; (b) a first layer means composed of a plurality of pairs of processing elements; (c) means interconnecting each said sensing means with a pair of said plurality of pairs of processing elements of said first layer for transmitting one input representative of said sensed signal to one processing element of each said pair of processing elements and another input representative of a phase shifted said sensed signal; and (d) a second layer means composed of a plurality of processing and learning elements, each of said first layer processing elements being connected to all of said second layer processing and learning elements for fanning out said sensed signal and said phase shifted sensed signal to all of the processing and learning elements of said second layer means, each processing and learning elements of said second layer means being capable of selecting out and learning different segments of the sensed signal in accordance with a predetermined competitive learning rule that partitions the signal into said different segments.

Claim 3

The system of claim 1 further comprising: a third layer means composed of a plurality of processing elements connected to said second layer means for processing said different segments to identify a desired characteristic of the emanated signal.

Claim 4

The system of claim 1 wherein said transmitting means includes: first and second conductive leads connecting said each sensing means with said processing elements of said first layer means; and a signal delay producing device disposed in one of said leads.

Claim 5

The system of claim 1 wherein said competitive learning rule is a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product

Claim 6

A signal phase sensitive neural network system, comprising: (a) a plurality of means for sensing a signal emanating from an environment of interest; (b) a first layer means composed of a plurality of processing elements connected to said plurality of sensing means for receiving inputs representative of said sensed signal; and (c) a second layer means composed of a plurality of processing and learning elements, each of said processing elements of said first layer means being connected to all of said processing and learning elements of said second layer means for fanning out said inputs to all of the processing and learning elements, each of said processing and learning elements being capable of selecting out and learning different segments of the signal in accordance with a predetermined competitive learning rule that partitions the signal into said different segments, said competitive learning rule being a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product.

Claim 7

A signal phase sensitive neural network system, comprising: (a) a plurality of means for sensing a signal emanating from an environment of interest; (b) a first layer means composed of a plurality of processing elements connected to said plurality of sensing means for receiving inputs representative of said sensed signal; and (c) a second layer means composed of a plurality of processing and learning elements, each of said processing elements of said first layer means being connected to all of said processing and learning elements of said second layer means for fanning out said inputs to all of the processing and learning elements, each of said processing and learning elements being capable of selecting out and learning different segments of the signal in accordance with a predetermined competitive learning rule that partitions the signal into said different segments, said competitive learning rule being a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product.

Claim 8

The system of claim 5 further comprising: a third layer means composed of a plurality of processing elements connected to said processing and learning elements of said second layer means for processing the different signal segments into identify a desired characteristic of the emanated signal.

Claim 9

A signal phased sensitive neural network method, comprising the steps of: (a) sensing a signal emanating from an environment of interest; (b) transmitting pairs of the sensed signal to each pair of a plurality of pairs of processing elements of a first layer thereof such that the signals of the sensed signal pair of substantial duplicates except that one is phase shifted by a predetermined amount relative to the other; (c) fanning out the sensed signal pair from the first layer processing elements to each of a plurality of processing and learning elements of a second layer; and (d) selecting out and learning different segments of the emanated signal, from the sensed signal pair at the second payer of processing and learning elements, in accordance with a predetermined competitive learning rule for partitioning the signal into the different segments.

Claim 10

A signal phased sensitive neural network method, comprising the steps of: (a) sensing a signal emanating from an environment of interest; (b) transmitting pairs of the sensed signal to each pair of a plurality of pairs of processing elements of a first layer thereof such that the signals of the sensed signal pair of substantial duplicates except that one is phase shifted by a predetermined amount relative to the other; (c) fanning out the sensed signal pair from the first layer processing elements to each of a plurality of processing and learning elements of a second layer; and (d) selecting out and learning different segments of the emanated signal, from the sensed signal pair at the second payer of processing and learning elements, in accordance with a predetermined competitive learning rule for partitioning the signal into the different segments.

Claim 11

The method of claim 7 wherein said competitive learning rule is a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product.

Claim 12

The method of claim 7 further comprising: transmitting the different signal segments from the second layer processing and learning elements to a plurality of processing elements of a third layer; and processing the different signal segments at the third layer to identify a desired characteristic of the emanated signal.

Claim 13

A signal phase sensitive neural network method, comprising the steps of: (a) sensing a signal emanating from an environment of interest; (b) transmitting inputs of the sensed signal to each of a plurality of processing elements of a first layer; (c) fanning out the inputs from the first layer processing elements to each of a plurality of processing and learning elements of a second layer; and (d) selecting out and learning different segments of the signal, form the inputs at the second layer, in accordance with a predetermined competitive learning rule for partitioning the signal into the different segments, said competitive learning rule being a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product.

Claim 14

A signal phase sensitive neural network method, comprising the steps of: (a) sensing a signal emanating from an environment of interest; (b) transmitting inputs of the sensed signal to each of a plurality of processing elements of a first layer; (c) fanning out the inputs from the first layer processing elements to each of a plurality of processing and learning elements of a second layer; and (d) selecting out and learning different segments of the signal, form the inputs at the second layer, in accordance with a predetermined competitive learning rule for partitioning the signal into the different segments, said competitive learning rule being a modified Kohonen competitive learning rule in accordance with the expression: W.sub.i.sup.new =W.sub.i.sup.old +α(x.sub.ik -c.sub.ik) d.sub.ik where W i is the weight plane, expressed as a bi-vector, of the ith PE of layer 2, α is the learning rate, d ik =x k ·W i the bar denotes normalization, c ik is the projection of x k onto W i , normalized to unit length, and the symbol denotes the wedge product.