Signal discrimination device using neural network
First Claim
1. A signal discrimination device using a neural network for classifying input data signals into a plurality of categories, said signal discrimination device comprising:
- input means for supplying input data signals;
parameter supply means for supplying distinct values of parameters for characterizing respective categories into which said input data signals are classified;
adaptive code generator means coupled to said parameter supply means to receive the distinct values of parameters supplied from said parameter supply means, and for generating codes for representing said respective categories on the basis of said values of said parameters supplied from said parameter supply means, wherein distances between said codes correspond to degrees of affinity between said respective categories;
neural network means coupled to said input means and responsive to said codes from said adaptive code generator means for outputting discrimination signals in response to said input data signals, wherein said discrimination signals correspond to said codes for representing said respective categories; and
discrimination result judgment means coupled to said neural network means and responsive to said codes for respective categories from said adaptive code generator, for determining said categories corresponding to respective input data signals by comparing said discrimination signals with said codes for respective categories.
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Abstract
A signal discrimination device using a neural network for discriminating input signals such as radar reception signals includes an adaptive code generator means for generating codes for representing the discrimination categories. The distances between the codes for closely related categories are smaller than the distances between the codes for remotely related categories. During the learning stage, the neural network is trained to output the codes for respective inputs. The discrimination result judgment means determines the categories by comparing the outputs of the neural network and the codes for the respective categories.
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Citations
7 Claims
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1. A signal discrimination device using a neural network for classifying input data signals into a plurality of categories, said signal discrimination device comprising:
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input means for supplying input data signals; parameter supply means for supplying distinct values of parameters for characterizing respective categories into which said input data signals are classified; adaptive code generator means coupled to said parameter supply means to receive the distinct values of parameters supplied from said parameter supply means, and for generating codes for representing said respective categories on the basis of said values of said parameters supplied from said parameter supply means, wherein distances between said codes correspond to degrees of affinity between said respective categories; neural network means coupled to said input means and responsive to said codes from said adaptive code generator means for outputting discrimination signals in response to said input data signals, wherein said discrimination signals correspond to said codes for representing said respective categories; and discrimination result judgment means coupled to said neural network means and responsive to said codes for respective categories from said adaptive code generator, for determining said categories corresponding to respective input data signals by comparing said discrimination signals with said codes for respective categories. - View Dependent Claims (2, 3, 4, 5)
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6. A signal discrimination device using a neural network for classifying input data signals into a plurality of categories, said signal discrimination device comprising:
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input means for supplying input data signals; parameter supply means for supplying distinct values of parameters for characterizing respective categories into which said input data signals are classified; adaptive code generator means coupled to said parameter supply means to receive the distinct values of parameters supplied from said parameter supply means, and for generating codes for representing said respective categories on the basis of said values of said parameters supplied from said parameter supply means, wherein distances between said codes correspond to degrees of affinity between said respective categories; neural network means coupled to said input means for outputting discrimination signals in response to said input data signals, wherein said discrimination signals correspond to said codes for representing said respective categories; discrimination result judgment means coupled to said neural network means for determining said categories corresponding to respective input data signals by comparing said discrimination signals with said codes for respective categories; and wherein a code length L of said codes for representing respective categories is equal to a number M of said parameters;
said parameters are numbered from 1 to M;
said categories are numbered from 1 to N, N being the number of said categories; and
said adaptive code generator means includes;means for determining a minimum Pj1 and a maximum Pj2 of jth parameter Pj, for respective values of j from 1 to M; and means responsive to said means for determining a minimum Pj1 and a maximum Pj2 for determining code Ci for an ith category by;
space="preserve" listing-type="equation">C.sub.ij =(P.sub.j -P.sub.j1)/(P.sub.j2 -P.sub.j1)where i ranges from 1 through N and j ranges from 1 through L.
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7. A signal discrimination device using a neural network for classifying input data signals into a plurality of categories, said signal discrimination device comprising:
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input means for supplying input data signals; parameter supply means for supplying distinct values of parameters for characterizing respective categories into which said input data signals are classified; adaptive code generator means coupled to said parameter supply means to receive the distinct values of parameters supplied from said parameter supply means, and for generating codes for representing said respective categories on the basis of said values of said parameters supplied from said parameter supply means, wherein distances between said codes correspond to degrees of affinity between said respective categories; neural network means coupled to said input means for outputting discrimination signals in response to said input data signals, wherein said discrimination signals correspond to said codes for representing said respective categories; discrimination result judgment means coupled to said neural network means for determining said categories corresponding to respective input data signals by comparing said discrimination signals with said codes for respective categories; and wherein a code length L of said codes for representing respective categories is equal to a number M of said parameters;
said parameters are numbered from 1 to M;
said categories are numbered from 1 to N, N being the number of said categories; and
said adaptive code generator means includes;means for determining from the distinct values of parameters supplied from said parameter supply means a minimum Pj1, a maximum Pj2, and a minimum separation dj of a jth one of said parameters, Pj, for respective values of j from 1 to M; means responsive to said means for determining a minimum for determining a code length Lj for said jth parameter by;
space="preserve" listing-type="equation">L.sub.j =INT((P.sub.j2 -P.sub.j1)/d.sub.j)where j ranges from 1 to M and a function INT() represents rounding fractions up; means responsive to said means for determining a code length for setting a total code length L equal to a sum of Lj for respective values of j from 1 to M; means responsive to said means for determining a minimum for determining a partial code cj for said jth parameter Pj for an ith category by;
##EQU15## where j and k range from 1 to M and from 1 to Lj, respectively, and cjk represents a kth component of said partial code cj ; andmeans responsive to said means for determining a partial code for determining a total code Ci for representing said ith category by;
space="preserve" listing-type="equation">C.sub.i =c.sub.1 ⊕
c.sub.2 ⊕
- - - ⊕
c.sub.mwhere i ranges from 1 to N and a symbol ⊕
represents a concatenation operator. X
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Specification