first of all , the differences between the present invention and the conventional methods will be described with reference to fig1 to 3 . fig1 and 2 show the orders of computing the pattern matchings of the horizontal and vertical algorithms . the former method requires large buffer memories and has a relatively long response time , whereas the latter method requires the transfer of n reference patterns for each frame of the input pattern , although the response time is reduced , so that a greater number of patterns can be transferred , as compared with the former horizontal algorithm . according to the method of the present invention , the sequence of computing the asymptotic equation ( 6 ) for solving the minimization problem of equation ( 3 ) is different from those of the horizontal and vertical algorithms of the prior art . according to the method of the present invention , more specifically , as shown in the flow charts of fig4 a and 4b , l time axes i of the input pattern are gathered into a block ii , in which : the dissimilarity g n ( j ) at i =( ii - 1 )· l is read out to execute computation for fixed j from i =( ii - 1 )· l + 1 to ii · l ( i . e ., the computation of one line : the computations of blocks 702 and 703 of fig4 b ) and to store the dissimilarity for i = ii · l in the g n ( j ); one line computation is executed from j =( ii - 1 )· l + 1 - r ( i . e ., the computation of l columns : the computations of blocks 701 , 702 , 703 and 704 of fig4 a and 4b ) and the computations of those l columns are advanced from ii = 1 to ii = i / l along the i - axis ( i . e ., the computation of block 705 of fig4 b ), thus deriving the value g n ( i , j ), i . e ., the normalized dissimilarity d ( a , b n ). since the computations of the l columns may be executed i / l times , according to the method of the present invention , the number of transfer of the n number of reference patterns is reduced to i / l so that the quantity of transfer is reduced to 1 / l , as compared with the vertical algorithm . since the computations of the l columns are advanced along the i - axis , moreover , the computation of the dissimilarity can be executed in synchronism with the input of the input pattern , and the computed result can be attained immediately after the input of the final frame of the input pattern so that the response time is shortened . still further , the pattern buffers required for the computation of the one line in this invention are those for the l frames of the input pattern and one frame of the reference pattern so that they are reduced to ( l + 1 )/( 2r + 2 ), as compared with that of the horizontal algorithm . as has been described above , the pattern matching system according to the present invention has the following advantages : a small quantity of transfer of the pattern data ; a small capacity of the pattern buffer memories ; and a short response time . next , the present invention will be described in the following in connection with one embodiment thereof with reference to the accompanying drawings . the pattern matching system according to the present invention is composed of , as shown in fig5 a distance computation unit 1 , an asymptotic equation computation unit 2 , and a pattern matching control unit 3 . the distance computation unit 1 is composed of : an input pattern buffer 11 for storing l frames of the input pattern ; a reference pattern buffer 12 for storing one frame of the reference pattern ; and an inter - vector distance computation unit 13 for deriving the inter - vector distance || a i - b j n || between the i - th frame a i of the input pattern and the j - th frame b j n of the reference pattern . the asymptotic equation computation unit 2 is composed of a work memory 21 , an arithmetic unit 22 , an asymptotic equation arithmetic control unit 23 and a dissimilarity memory 24 . the pattern matching control unit 3 controls the distance computation unit 1 , the asymptotic equation computation unit 2 , an input pattern memory 4 and a reference pattern memory 5 in accordance with the time chart shown in fig8 . the value g n ( 0 ) in the dissimilarity memory 24 is cleared in response to a signal c l . next , the (( ii - 1 )· l + 1 ) th to ii · l - th frames of the input pattern are transferred from the input pattern memory 4 to the input pattern buffer 11 . furthermore , the j - th frame of the n - th reference pattern , which is indicated by signals n and j , is transferred to the reference pattern buffer 12 . subsequently , the value g n ( j ) of the dissimilarity memory 24 is read out and stored in the g n (( ii - 1 )· l , j ) address of the work memory 21 . subsequently , in response to a signal d st generated by the pattern matching control unit 3 , the inter - vector distance arithmetic unit 13 conducts the computation of || a i - b j n ||. the inter - vector distance is derived from the following equation , for example : ## equ3 ## the inter - vector distance arithmetic unit 13 operates , as shown in fig6 and 9 : first to clear an accumulator 135 in response to a signal d cl generated from an inter - vector distance arithmetic control unit 131 ; to read out the values a ik and b jk n from the input pattern buffer 11 and the reference pattern buffer 12 in response to a signal k varying from 1 to k , which is generated by the inter - vector distance arithmetic control unit 131 ; to derive the value | a ik - b jk n | by a subtractor 132 and an absolute value circuit 133 ; and to derive the summation of | a ik - b jk n | by an adder 134 and the accumulator 135 . here , the l - th frame of the input buffer 11 is stored with the (( ii - 1 ) l + 1 ) th frame of the input pattern . subsequently , the arithmetic unit 22 shown in fig7 computes the asymptotic equation ( 6 ) by the use of both the distance d ( i , j ) attained by the inter - vector distance arithmetic unit 13 and the value g n ( i , j ) of the work memory 21 . the asymptotic equation arithmetic control unit 23 outputs signals j 1 , j 2 and j 3 , as shown in the time chart of fig1 , in response to the signal i ( i =( ii - 1 )· l + 1 ), which is generated by the pattern matching control unit 3 , and reads out and sets the values g n ( i - 1 , j - 2 ), g n ( i - 1 , j - 1 ) and g n ( i - 1 , j ) in registers 221 , 222 and 223 , respectively . a comparator 224 compares the registers 221 , 222 and 223 to output a minimum . an adder 225 adds the distance d ( i , j ) and the output of the comparator 224 so that the resultant summation is written as g n ( i , j ). the pattern matching control unit 3 controls the distance computation unit 1 and the asymptotic equation computation unit 2 in accordance with the time chart shown in fig8 to derive the computations of the asymptotic equation ( 6 ) for the l from 1 to l thereby to conduct the computation of the one line . subsequently , the pattern matching control unit 3 reads out the value g n ( ii · l , j ) from the work memory 21 and stores it in the g n ( j ) of the dissimilarity memory 24 . subsequently , the control unit 3 derives the value j from its lower limit l ( ii ) to its upper limit u ( ii ) to conduct computations of the l columns . subsequently , the control unit 3 derives the value n from 1 to n to conduct computations the l of columns of the n reference patterns . the control unit 3 derives the value ii from 1 to i / l to attain the value g n ( i , j ). here , the lower limit l ( ii ) and the upper limit u ( ii ) indicate the limits of the range for computing the asymptotic equation , as shown in fig3 and are given by the following equations : the present invention has been described hereinbefore in connection with the embodiment thereof but should not have its scope limited to the foregoing description . especially in the description of the present invention , the work memory 21 can be replaced by a ring buffer comprising 3l memories . specifically , if the computation of the j - th line uses g n ( i , j )→ x , g n ( i , j - 1 )→ y , and g n ( i , j - 2 )→ z , the computation of the ( j + 1 ) th line can be conducted by using g n ( i , j + 1 )→ z , g n ( i , j )→ x , and g n ( i , j - 1 )→ y . on the other hand , the simple equation ( 6 ) was used as the asymptotic equation but may be replaced by another arbitrary asymptotic equation . for example , it is conceivable to use a method using the following equations : ## equ4 ## in this case , however , 2 ·( 2r + 1 ) n memories for the two columns are required as the dissimilarity memories because of necessity of the dissimilarity data until the ( i - 2 ) th column . moreover , the distance between the input pattern a i and reference pattern b j n was described by using the distance measure of equation ( 4 ) but may apparently be replaced by a euclidean distance , as is expressed by the following equation ( 11 ), or by an inner product , as is expressed by the following equation ( 12 ): ## equ5 ##