Adaptive fuzzy frequency hopping system

Claim 2

A fuzzy logic spread spectrum communications system comprising: fuzzy logic spreader for spreading an input signal over a range of frequencies and fuzzy logic despreader for extracting the spread input signal from the range of frequencies.

Claim 3

A fuzzy logic frequency spreader comprising: a fuzzy logic pseudo-random number generator and frequency synthesizer means, responsive to the fuzzy logic pseudo-random number generator, for upconverting an input signal to a range of frequencies.

Claim 4

A fuzzy logic frequency spreader comprising: a fuzzy logic pseudo-random number generator and frequency synthesizer means, responsive to the fuzzy logic pseudo-random number generator, for upconverting an input signal to a range of frequencies.

Claim 5

A fuzzy logic despreader comprising: a fuzzy pseudo-random number generator and means, responsive to the fuzzy pseudo-random number generator, for downconverting a fuzzy spread received signal.

Claim 6

A fuzzy logic despreader comprising: a fuzzy pseudo-random number generator and means, responsive to the fuzzy pseudo-random number generator, for downconverting a fuzzy spread received signal.

Claim 7

A fuzzy logic pseudo-random number generator comprising: first means for generating a plurality of pseudo-random numbers out of a predetermined range of N distinct possible integer numbers over each of a plurality of predetermined time intervals; second means for partitioning the predetermined range of N distinct numbers into n bins, each bin containing N/n of the possible distinct numbers; third means for sampling the pseudo-random numbers to provide a plurality of pseudo-random samples having a minimum auto-correlation and a minimum time delay therebetween; fourth means for counting the number of samples that fall into each bin to provide a count for each bin; fifth means for defining a number of fuzzy logic sets for each bin relating to the number of samples in each bin; sixth means for mapping the count in each bin into the sets, each count belonging to each set in some proportion and the total extent to which each count belongs to all of the sets being equal to unity; seventh means for applying fuzzy logic if-then rules to the sets to determine a firing level of each rule over each predetermined time interval, the rules covering a graph of a random function, each conjunct in each antecedent of each rule being equal to a fit value corresponding to the extent to which each count belongs to each set; eighth means for determining an extent to which each rule is activated by identifying a minimum fit value for antecedent of each rule; ninth means for weighting the consequent of each rule in proportion to the extent to which the antecedent of each rule is activated; and tenth means for summing each of the weighted consequents to create a set of pseudo-random fuzzy logic output numbers, each number corresponding to an associated bin.

Claim 8

A fuzzy logic pseudo-random number generator comprising: first means for generating a plurality of pseudo-random numbers out of a predetermined range of N distinct possible integer numbers over each of a plurality of predetermined time intervals; second means for partitioning the predetermined range of N distinct numbers into n bins, each bin containing N/n of the possible distinct numbers; third means for sampling the pseudo-random numbers to provide a plurality of pseudo-random samples having a minimum auto-correlation and a minimum time delay therebetween; fourth means for counting the number of samples that fall into each bin to provide a count for each bin; fifth means for defining a number of fuzzy logic sets for each bin relating to the number of samples in each bin; sixth means for mapping the count in each bin into the sets, each count belonging to each set in some proportion and the total extent to which each count belongs to all of the sets being equal to unity; seventh means for applying fuzzy logic if-then rules to the sets to determine a firing level of each rule over each predetermined time interval, the rules covering a graph of a random function, each conjunct in each antecedent of each rule being equal to a fit value corresponding to the extent to which each count belongs to each set; eighth means for determining an extent to which each rule is activated by identifying a minimum fit value for antecedent of each rule; ninth means for weighting the consequent of each rule in proportion to the extent to which the antecedent of each rule is activated; and tenth means for summing each of the weighted consequents to create a set of pseudo-random fuzzy logic output numbers, each number corresponding to an associated bin.

Claim 9

The invention of claim 4 including eleventh means for defuzzifying the set of pseudo-random fuzzy logic output numbers to provide an output pseudo-random number.

Claim 10

The invention of claim 5 wherein the eleventh means includes twelfth means for determining a centroid of the set of pseudo-random fuzzy logic output numbers.

Claim 13

The invention of claim 7 including the step of defuzzifying the set of pseudo-random output numbers to provide an output pseudo-random number.

Claim 14

The invention of claim 8 wherein the step of defuzzifying the set of pseudo-random output numbers to provide an output pseudo-random number includes the step of determining a centroid of the set of pseudo-random output numbers.

Claim 15

The invention of claim 9 wherein the step of determining the centroid includes the step of rotating the set of pseudo-random output numbers so that a maximum output number is in a middlemost bin.

Claim 16

The invention of claim 10 wherein the step of determining the centroid includes the step of subtracting the smallest value in the output set from each value in the output set to provide a noise output set.

Claim 17

The invention of claim 11 wherein the step of determining the centroid includes the step of finding a centroid of noise output set.

Claim 18

The invention of claim 12 wherein the step of determining the centroid includes the step of combining the centroid of the noise output set with a previous output number scaled by 1/n to provide the output number.

Claim 19

The invention of claim 7 including the step of adapting the consequents of each rule.

Claim 20

The invention of claim 14 wherein the step of adapting the consequents of each rule includes the step of scaling and truncating a predetermined number of samples from a uniform random number generator to provide a plurality of integers between 1 and n.

Claim 21

The invention of claim 15 wherein the step of adapting the consequents of each rule includes the step of providing a distribution of previous samples.

Claim 22

The invention of claim 16 wherein the step of adapting the consequents of each rule includes the step of identifying an antecedent of one of the rules which has a maximum fit value with the distribution of previous samples.

Claim 23

The invention of claim 17 wherein the step of adapting the consequents of each rule includes the step of associating a next sample with the antecedent of one of the rules which has a maximum fit value with the distribution of previous samples.

Claim 24

The invention of claim 18 wherein the step of adapting the consequents of each rule includes the step of associating each of the predetermined number of samples with the antecedent of one of the rules which has a maximum fit value with a distribution of previous samples to provide a histogram of output bin values for each antecedent of each rule.

Claim 25

The invention of claim 19 wherein the step of adapting the consequents of each rule includes the step of normalizing the histograms to provide the adapted consequents of the rules.