Method for memorizing membership functions and the related circuit for calculating the grade of membership of the antecedents of fuzzy rules

Abstract: A method for memorizing membership functions (MF), comprising the following steps: memorizing the position of the vertex (CV) of the triangle that defines the function in the universe of discourse (UdD); memorizing a first distance (LVD) between the position of the vertex in the universe of discourse and the point of intersection between the left side of the triangle that defines the membership function and the axis of the universe of discourse; memorizing a second distance (RVD) between the position of the vertex in the universe of discourse and the point of intersection between the right side of the triangle that defines the membership function and the axis of the universe of discourse. The present invention furthermore relates to a circuit for calculating the grade of membership of the antecedent of a fuzzy rule, the particularity whereof resides in that it comprises means adapted to fuzzify an input variable by adopting the geometric proportions that occur between homologous sides of similar triangles defined by the position of the input value in the universe of discourse.

Description: The present invention relates to a method for memorizing membership functions and to the related circuit for calculating the grade of membership of the antecedents of fuzzy rules. Any fuzzy rule is constituted by an antecedent part and by a consequent part or conclusion: the antecedent has a grade of membership of its own determined on the basis of the value assumed by the inputs, following the semantics of the rule itself. The antecedent part can be composed of a plurality of terms: in order to determine the weight of the individual term, according to the meaning assumed in fuzzy logic, it is necessary to determine the highest value of the intersection between the membership function and the generic input. In general, the grade of membership α is defined as follows: α = max x {min [A,A']} where A and A' respectively designate the input sets and the corresponding membership function, as shown in figure 1. In the case of crisp inputs, with reference to figure 2, the value α is determined by the segment produced by the intersection between the straight line A defined by the equation UdD = i (where i is the input of the system and UdD is the universe of discourse) and the membership function A'. Performing this calculation entails using a method for memorizing the membership function, which has a triangular shape in the example of figure 1, so that it is possible to circuitally perform the intersection with the input in the simplest and most economical manner in terms of hardware. There are various methods for memorizing membership functions and for their fuzzification. One of these methods consists in memorizing all the points of the membership function by means of a look-up table. Another method is the memorizing of triangular membership functions by means of the gradients and the central vertex of the triangle. DE-3936503A1 discloses such a method for memorizing membership functions in which the membership function is memorized using the position of the vertex of the triangle that defines the function, the gradients of the sides of the triangle and coefficients related to the shape of the triangle. Another method is the memorizing of trapezoidal membership functions by means of the two vertices and of the distance, relative to the nearest vertex, of the points of intersection between the two sides of the triangle and the axis representing the universe of discourse (UdD), as described in the article "A parametrized fuzzy processor and its applications", by B.T. Chen et al., published in the journal "Fuzzy Sets and Systems", Elsevier Science Publishers B.V., no. 59, 1993, pages 149-172. IEICE TRANSACTIONS ON ELECTRONICS, vol. E76-c, No. 7, 3, pages 1102-1111, K. Nakamura: "A 12-bit Resolution 200 kFLIPS Fuzzy Inference Processor" discloses a method as defined in the preamble of claim 1. An aim of the present invention is to provide a method for memorizing membership functions and the related circuit for calculating the grade of membership of the antecedent of the fuzzy rules that allows to perform the intersection between the membership function and the input in the simplest and most economical manner in terms of hardware. Within the scope of this aim, an object of the present invention is to provide a method capable of memorizing a kind of information capable of describing a membership function that is less onerous in terms of used bits. Another object is to provide a method that is advantageous, in terms of time and computing simplicity, for calculating the grade of membership a of the antecedent of a fuzzy rule. Another object is to provide a circuit for calculating the grade of membership a that is simpler than known circuits and can be implemented with a smaller silicon area. Another object of the present invention is to provide a method and a circuit that are highly reliable, relatively easy to manufacture, and at competitive costs. This aim, these objects, and others which will. become apparent hereinafter are achieved by a method for memorizing membership functions, as defined in claim 1. The characteristics and advantages of the invention will become apparent from the description of a preferred but not exclusive embodiment thereof, illustrated only by way of non-limitative example in the accompanying drawings, wherein: figure 1 is an example of an intersection between a generic input and a membership function; figure 2 is an example of an intersection between a crisp input and a membership function; figure 3 is a parametric representation of a membership function of the trapezoidal type in accordance with the present invention; figure 4 is a parametric representation of possible membership functions of the trapezoidal type; figure 5 is a parametric representation of possible membership functions of the right-angled trapezoidal type; figure 6 is a parametric representation of possible membership functions of the hexagonal or pentagonal type, framed at the borders of the universe of discourse; figure 7 is a parametric representation of possible membership functions of the triangular type; figure 8 is a parametric representation of possible membership functions of the horizontal type; figure 9 is a parametric representation of possible membership functions of the crisp type; figure 10 is a parametric representation of a membership function used to calculate the grade of membership α by using similar triangles for the condition I ≤ CV; figure 11 is another parametric representation of a membership function used to calculate the grade of membership α by using similar triangles for the condition I > CV; figure 12 is a flowchart of the algorithm of a first embodiment of the method, used to calculate the grade of membership α; figure 13 is a circuit block diagram of a first embodiment of the circuit, related to the first embodiment of the method shown in figure 12; figure 14 is a flowchart of the algorithm of a second embodiment of the method, used to calculate the grade of membership α; figure 15 is a circuit block diagram of a second embodiment of the circuit, related to the first embodiment of the method shown in figure 14; figure 16 is a flowchart of the improved version of the second embodiment of the method, shown in figure 14; figure 17 is a block diagram of the activation circuit of the improved version of the method, shown in figure 16; figure 18 is a parametric representation of a triangular membership function; figure 19 shows examples of triangular membership functions in which the vertex is at the peak of the degree of truth; figure 20 shows examples of membership functions of the pentagonal type framed at the borders of the universe of discourse, with the restriction of the distance LVD and RVD; figure 21 shows examples of membership functions having the shape of right-angled trapezoids with the distance restriction LVD or RVD; figure 22 shows examples of horizontal membership functions when the number of bits that defines CV is greater than, or equal to, the number of bits that describes the degree of truth; figure 23 shows examples of horizontal membership functions of a first case if the number of bits that defines CV is smaller than the number of bits that describes the degree of truth; figure 24 shows examples of horizontal membership functions of a second case, if the number of bits that defines CV is smaller than the number of bits that describes the degree of truth; figure 25 is an example of a crisp function; figure 26 is a parametric representation of a triangular membership function with I > CV; figure 27 is a parametric representation of a triangular membership function with I ≤ CV; figure 28 is a flowchart of a third improved embodiment of the method, adapted to calculate the negation of α; figure 29 is a diagram of the activation circuit of the method according to figure 28. In order to describe membership functions (MF), it is necessary to memorize some parameters of the membership function, and their choice must be made carefully to have the lowest possible cost in terms of bits. Usually, the higher the number of bits introduced, the larger the number of membership functions that can be represented. We will first of all consider the memorizing of triangles and trapezoids. The memorizing method for trapezoids according to the present invention is based on the observation that a trapezoid can be considered as a truncated triangle. With reference to figure 3, the characteristic parameters that allow to unambiguously identify membership functions represented by trapezoids are the position of the vertex CV in the universe of discourse UdD, the distances related to the vertex, that is to say, the distances LVD and RVD, the altitude of the triangle HV, the altitude of the trapezoid HT, and the maximum value of the degree of truth max. The top part of figure 3 shows a memory word in which the following items are respectively memorized: CV: position, in the universe of discourse, of the vertex of the triangle that defines the trapezoidal membership function; LVD: the leftward distance between the vertex CV and the point of intersection between the membership function and the axis of the universe of discourse, along said axis; RVD: the rightward distance between the vertex CV and the point of intersection between the membership function and the axis of the universe of discourse, along said axis; HV: altitude of the triangle that is used to describe the membership function; HT: altitude of the membership function; whereas the maximum value of the degree of truth max, that is to say the maximum value that the membership function can assume, is a known value for all membership functions. The value HT is always smaller than, or equal to, the value of max. The dimensions of the above mentioned parameters are a function of the number of membership functions to be described; accordingly, the present description neither limits not specifies the optimum dimensions in terms of bits used. However, in order to have a large number of membership functions, it is recommended that CV have a number of bits equal to the number of bits that discretizes the UdD, that LVD and RVD have a greater or equal number, and that HV be greater than, and HT be equal to, the number of bits that discretize the degree of truth, that is to say, the value max. The above recommended conditions are assumed to be met hereinafter, but the following exposition prescinds therefrom. In order to describe degenerate membership functions without increasing the number of bits of the above mentioned parameters, the following conditions are set: a) if LVD or RVD is equal to 0, the plot of the membership function is horizontal in the corresponding left or right side; b) if LVD and RVD are both equal to zero, the plot of the membership function is horizontal throughout the UdD, with the degree of truth equal to the value indicated in the bits that indicate the altitude HT of the membership function; c) if there is a perfectly vertical left or right side, which should be described with LVD or RVD = 0, it is instead described as a side with minimal gradient, that is to say, with LVD or RVD = 1, since the same result is yielded for calculating the grade of membership α. When HT and HV vary, it is possible to describe trapezoidal membership functions, triangular membership functions, and pentagonal or hexagonal membership functions at the borders of the universe of discourse (UdD). All the membership functions that can be represented are listed hereafter: (i) trapezoidal membership functions, as shown in figure 4, in which the shorter parallel side is at the value HT, which can be equal to the value max (function MF1) or lower than said value (function MF2); (ii) membership functions of the right-angled trapezoid type, as shown in figure 5, in which the shorter parallel side is equal to, or smaller than, the maximum value max of the degree of truth; it should be noted that for calculating the grade of membership α, the perfectly vertical membership function behaves like the membership function that has the minimal slope, that is to say, with LVD = RVD = 1, and therefore the membership function MF1, shown in figure 5, with a perfectly vertical left side, is defined with LVD = 1, that is to say, with the minimal slope, whereas the function MF2, which has a perfectly vertical right side, is defined with RVD = 1, since as mentioned above they have an identical behavior in the calculation of the grade of membership α; (iii) membership functions of the hexagonal or pentagonal type, as shown in figure 6, framed at the borders of the UdD, in which the altitude HT is smaller than the value max of the degree of truth (MF2) or is equal to said value (MF1); (iv) membership functions of the triangular type, as shown in figure 7, in which the value of the vertex is smaller than the maximum value max of the degree of truth (MF2) or is equal to said value (MF1); (v) horizontal membership functions, as shown in figure 8; in this case, the value HT indicates the grade of membership α; (vi) crisp membership functions, such as the one shown in figure 9, which is always zero except in CV, where it assumes the value of the maximum degree of truth max; these membership functions are not represented with LVD = RVD = 0, as would seem to be more logical, but with LVD = RVD = 1, which behaves, in terms of the calculation of the value of α, like the crisp function and is used as such, whereas the coding LVD = 0 or RVD = 0 is given the meaning of a horizontal side. The calculation of the grade of membership α, that is to say, the fuzzification of the input value, is achieved by adopting the geometric proportions that occur among homologous sides of similar triangles. If I is the input to be fuzzified, when I ≤ CV the situation shown in figure 10 is obtained. As regards the similar right-angled triangles V-CV-LV and IP-I-LV, the following proportion can be written: a : (I - (CV - LVD)) = HV : LVD from which: a = [HV * (I - (CV - LVD))] / LVD This sets the condition: if a > HT then α = HT else α = a. In another manner, α can be calculated by considering the similar right-angled triangles V-P-IP and V-CV-LV. In this case, one obtains: A : HV = (CV - I) : LVD and from this one obtains the value of A: A = [HV * (CV - I)] / LVD Accordingly: if A > HV then a = 0 else a = HV - A. This check is performed because if the input I is smaller than LV, the value A thus obtained is greater than HV; in this case, the value of α is zero. Furthermore, if a > HT then α = HT else α = a. For the condition I > CV, the situation is similar to the one shown in figure 11. As regards the similar right-angled triangles V-CV-RV and IP-I-RV, the following proportion can be written: a : (CV + RVD - I) = HV : RVD from which: a = [HV * (CV + RVD - I)] / RVD Accordingly: if a > HT then α = HT else α = a In this case, too, α can be calculated in another manner, by considering the similar right-angled triangles V-P-IP and V-CV-RV. In this case one obtains: A : HV = (I - CV) : RVD and from this proportion the value of A is obtained: A = [HV * (I - CV)] / RVD Accordingly: if A > HV then a = 0 else a = HV - A. This check is performed because if the input I is greater than RV, the value A thus obtained is greater than HV; in this case, the value of α is zero. Furthermore, if a > HT then α = HT else α = a To conclude, according to the above statements, the value of α can be calculated by means of the following formulae: If the input I is smaller than, or equal to, CV: a = [HV * (I - CV + LVD)] / LVD If a > HT then α = HT else α = a; If the input I is greater than CV: a = [HV * (CV + RVD - I)] / RVD If a > HT then α = HT else α = a. These formulae entail performing two subtractions, one multiplication, and one division inside the device used for calculation. It should also be noted that once the membership function has been set, the values CV, HV, HT, LVD, and RVD are fixed, whereas the value of the input I varies. Owing to the similar form of equations (1) and (2), the flowchart of a calculation algorithm for the first embodiment is similar to the one shown in figure 12. Initially, the input I is provided to the block 1, which checks whether the given input value is greater than CV or not. If it is smaller than CV or equal thereto, the block 2 assigns the parameters of equation (1) to the parameters A, B, and D (more specifically, A = I, B = CV, and D = LVD). Otherwise, if I > CV, the parameters of equation (2) are assigned to the parameters A, B, and D by means of the block 3 (more specifically, A = CV, B = I, and D = RVD). The block 4 uses the parameters A, B, and D of the blocks 2 and 3 to introduce them in the generic formula of equations (1) and (2), that is to say, in the formula a = (HV * (A -B + D)) / D. The result a, which is the value of the degree of truth that corresponds to the input value I determined on the basis of the triangle defining the membership function (see figures 10 and 11), is sent to the block 5, which tests whether this value of a is greater than the value of HT. If it is, the grade of membership α is set, by means of the block 6, to the value of HT; if a is equal to, or smaller than, HT, the block 7 assigns the value of a to α. The block diagram of the circuit that calculates the value of α according to the first embodiment of the method is shown in figure 13. The circuit comprises a first input register 8 in which the current value of the input variable I is memorized. There are also five other registers 9-13 for memorizing respectively the values of LVD, CV, RVD, HV, and HT. These are the values related to the membership function at issue, and remain unchanged as the input variable I assumes constantly changing values. The register 8 of the input variable I and the register 10 of the value CV are connected to a modulus comparator 14, which tests the condition I > CV of the block 1 of figure 12. On the basis of the result provided by the comparator 14, the multiplexers 15a-c assign the values of I, CV, and LVD or of CV, I, and RVD to the parameters A, B, and D respectively. More specifically, the multiplexers perform the function of the blocks 2 and 3 of figure 12. The outputs of the multiplexers 15a and 15b that provide respectively the values of the parameters A and B are sent to a subtracter 16 to produce the function A - B. The output of the subtracter 16 and the output of the multiplexer 15c that provides the parameter D are sent to an adder 17 that supplies the result of the operation A - B + D. The output of the adder 17 and the contents of the register 12 of the value HV are sent to a multiplier 18, which produces the result of the function HV * (A - B + D). The output of the multiplier 18 and the value D provided by the multiplexer 15c are sent to a divider 19, which provides the parameter a = (HV * (A - B + D)) / D. The output of the divider 19 is sent, together with the value of HT contained in the register 13, to a second modulus comparator, which tests the condition a > HT. On the basis of the result thus obtained, a fourth multiplexer 21 provides the value of HT or of a as the value of the grade of membership α. If the second calculation method described above is used, the formulae to be used are: If the input I is smaller than, or equal to, CV: A = [HV * (CV - I)] / LVD if A > HV then a = 0 else a = HV - A, if a > HT then α = HT else α = a. If the input I is greater than CV: A = [HV * (I - CV)] / RVD if A > HV then a = 0 else a = HV - A, if a > HT then α = HT else α = a. These formulae entail performing two subtractions, one multiplication, and one division inside the circuit used for calculation. As in the previous embodiment, the membership function is set with the values CV, HV, HT, LVD, and RVD, whereas the input value I varies. The flowchart of this embodiment of the method is shown in figure 14. Initially, the input I is supplied to the block 22, which tests whether the given input value is greater than CV or not. If it is smaller than, or equal to, CV, the block 23 assigns the parameters of equation (3) to the parameters A, B, and D (more specifically, A = CV, B = I, and D = LVD). Otherwise, if I > CV, the parameters of equation (4) are assigned by the block 24 to the parameters A, B, and D (more specifically, A = I, B = CV, and D = RVD). The block 25 uses the parameters A, B, and D of the blocks 23 and 24 to introduce them in the generic formula of equations (3) and (4), that is to say, in the formula A = (HV * (A - B)) / D. The result A, which is equal to the value HV - a (see figures 10 and 11), is sent to the block 26, which tests whether this value of A is greater than the value of HV. If it is, the value a is set to zero by means of the block 27; if the value of A is equal to, or smaller than, HV, then the block 28 assigns the value of HV - A to a. The result a is sent to the block 29, which tests whether this value of a is greater than the value of HT. If it is, the grade of membership α is set, by means of the block 30, to the value of HT; if it is equal to, or smaller than, HT, then the block 31 assigns the value of a to α. The block diagram of the circuit that calculates the value of α according to the second embodiment of the method is shown in figure 15. As in the previous case, the input variable I is memorized in the register 8, and the values LVD, CV, RVD, HV, and HT are memorized respectively in the registers 9-13. The modulus comparator 14 tests the condition I > CV, and the multiplexers 15a-c assign the values of equations (3) and (4) to the parameters A, B, and D respectively. More particularly, if the condition I > CV is met, the parameters of equation (4) are assigned to the parameters A, B, and D, that is to say, A = I, B = CV, and D = RVD. If instead the condition I > CV is not met, the parameters of equation (3) are assigned to the parameters A, B, and D, that is to say, A = CV, B = I, and D = LVD. The outputs of the multiplexers 15a and 15b are sent to the subtracter 16, which calculates the function A - B. The output of the subtracter 16, together with the value contained in the register 12, is sent to a multiplier 32, which calculates the function HV * (A - B). The output of the multiplier 32, together with the output of the multiplexer 15c, is sent to a divider 33, which provides the result of the function A = (HV * (A -B)) / D. The value of A, together with the value of HV memorized in the register 12, is sent to a second modulus comparator 34, which performs the comparison A > HV. On the basis of the result obtained, a fourth multiplexer 35 assigns the value of HV - A, calculated by a subtracter 36, or the value of zero (logic zero), to the variable a. The value of the variable a is sent, together with the value of HT memorized in the register 13, to a third modulus comparator 36, which tests the condition a > HT. On the basis of the result obtained, a fifth multiplexer 37, which receives in input the output of the fourth multiplexer 35 and the output of the register 13, assigns the value of a or the value of HT to the grade of membership α. In this embodiment, the hardware execution seems to be more onerous than the preceding one, but a third embodiment that offers a further simplification, leading to better results regarding the calculation of the grade of membership α, will be described hereinafter. In order to further simplify and reduce the hardware area dedicated to the calculation of α, a further refinement is introduced which allows to replace the hardware divider with an arithmetic shift in binary logic, changing the parameters to be memorized. Assuming that the maximum value of LVD and RVD is LVD = RVD = 2 n - 1, that is to say, LVD and RVD described with n bits, the following constants are introduced: KL = (HV * 2 n ) / LVD KR = (HV * 2 n ) / RVD With the first embodiment of the method, the following values would have to be memorized as significant parameters of the membership function: KL, LVD, CV, KR, RVD, HT and the formulae for calculating α become: If the input I is smaller than, or equal to, CV: a = [KL * (I - CV + LVD)] / 2 n if a > HT then α = HT else α = a; If the input I is greater than CV: a = [KR * (CV + RVD - I)] / 2 n if a > HT then α = HT else α = a. These formulae entail performing one subtraction, one addition, one multiplication, and one rightward shift of n bits inside the device that performs the calculation. The second embodiment of the method yields better results because only the following values are memorized as significant parameters of the membership function: KL, CV, KR, HV, HT and the formulae for calculating α are: If the input I is smaller than, or equal to, CV: A = [KL * (CV - I)] / 2 n if A > HV then a = 0 else a = HV - A, if a > HT then α = HT else α = a; If the input I is greater than CV: A = [KR * (I - CV)] / 2 n if A > HV then a = 0 else a = HV - A, if a > HT then α = HT else α = a. Accordingly, there are only two subtracters and one multiplier inside the device that performs the calculation, and the division by the value LVD or RVD has been replaced by the division by 2 n . The advantage of the introduction of the constants KL and KR is that it is possible to perform the division by 2 n simply by means of a rightward logic shift of n bits, which in hardware is performed simply by truncating the n less significant bits of the output bus of the multiplier. However, it should be noted that by truncating the n less significant bits one performs a division of integers by 2 n in which the remainder is constituted by the n eliminated bits. In order to obtain greater precision, the integer division, in this particular case the division by 2 n , can be performed by over- or under-approximating. If the remainder is smaller than half of the divider, that is to say, smaller than 2 n-1 , then under-approximation is performed and the quotient is taken directly. If instead the remainder is greater than, or equal to, 2 n-1 , then over-approximation is performed and 1 is added to the quotient. In hardware, this division with approximation is performed simply by truncating the n less significant bits and adding the nth truncated bit, since if the nth bit is 1, then the remainder (that is to say, the n bits that are removed) is greater than, or equal to, 2 n-1 , and therefore over-approximation is performed by adding 1 to the quotient; otherwise, under-approximation is performed by adding 0. It is specified that the division is eliminated inside the device that performs the calculation by introducing the constants KL and KR, whereas the parameters that identify the membership function are LVD, CV, RVD, HV, and HT. In the calculation of KL or KR, which is performed in software during compiling, multiplication by 2 n is performed so as to avoid losing precision in the division of integers by LVD or RVD, which during compiling is always performed as a division by integers with over- or under-approximation. The flowchart of the improved version of the second embodiment of the method is shown in figure 16. Initially, the condition I > CV is tested by the block 38. If this condition is true, the parameters of equation (8) are assigned to the parameters A, B, and D by means of the block 39, that is to say, A = I, B = CV, and D = KR. If instead the condition I > CV is false, the values of equation (7) are assigned to the parameters A, B, and D by means of the block 40, that is to say, A = CV, B = I, and D = KL. The parameters A, B, and D are then introduced, by means of the block 41, in the generic formula of equations (7) and (8), that is to say, in the formula C = (D * (A - B)). The value C is sent to the block 42, which determines the value A of figures 10 and 11. The n less significant bits are truncated from the value C, and the most significant bit of the n truncated less significant bits is added to the result. In this manner the value A is obtained, which is sent to the block 43, which tests the condition A > HV. If this condition is met, the value a is set to zero by means of the block 44. If instead the condition A > HV is false, the value a is set to HV - A by means of the block 45. The value of a is sent to the block 46, which tests the condition a > HT. If this condition is met, the grade of membership α is set to the value of HT by means of the block 47. If instead the condition a > HT is false, the grade of membership α is set to the value of a. The block diagram of the activation circuit of the improved version of the method of figure 16 is shown in figure 17. The circuit is substantially identical to the circuit for the activation of the second embodiment of the method, shown in figure 15, with the exception that the memorized parameters of the membership function are now KL, CV, KR, HV, and HT, which are memorized in the registers 9-13 respectively. The other exception is that an adder 48 now replaces the divider 33 and receives in input the bits that have not been eliminated by the division by 2 n and the most significant bit of the n unused truncated less significant bits. In particular, the modulus comparator 14 tests the condition I > CV, which in turn, on the basis of the result obtained, drives the multiplexers 15a-c, which assign the parameters of equations (7) and (8) to the parameters A, B, and D, that is to say, the function performed by the blocks 39 and 40 of figure 16. The values of the parameters A and B are sent by the multiplexers 15a and 15b to the subtracter circuit 16, which calculates the function A - B. The result of this function is multiplied by the multiplier 32 together with the value of the parameter D originating from the multiplexer 15c. At the output of the multiplier 32, the n less significant bits are eliminated, except for the most significant one, which is added to the output of the multiplier 32 by means of the adder 48. In this manner, the parameter A of figures 10 and 11 is obtained and is then processed as in the embodiment of figure 15. The memorizing of parameters of the triangular and pentagonal or hexagonal membership function at the borders of the universe of discourse, and the corresponding calculation of the value of α, will now be considered. Memorizing the above mentioned membership functions allows us to describe a greater number of membership functions, but by introducing restrictions to the parameters that describe the membership functions it is possible to describe subsets of membership functions with a smaller number of bits of information. This description will not discuss all the various possible cases, but will merely describe a particular case in which the ratio between the membership functions and the information bits is very high. In describing trapezoidal membership functions, by memorizing the altitude of the triangle and of the trapezoid, assuming HV = HT = value max of the degree of truth, it is possible to describe, with a reduced number of information bits, the triangular, pentagonal, or right-angled trapezoidal membership functions at the borders of the UdD the vertex whereof is at the maximum value of the degree of truth. In this case, the representation of the membership functions is based on memorizing only three characteristic parameters of the membership function, setting the altitude of the vertex of the membership function to the value max of the degree of truth. The characteristic parameters allowing to unambiguously identify the membership functions are the position of the vertex (CV) in the UdD and the distance related to the vertex of the points of intersection between the two sides of the triangle and the axis that represents the UdD, that is to say, the distances LVD and RVD, in other words: LVD: the leftward distance between the vertex CV and the point of intersection between the membership function and the axis of the universe of discourse, along said axis; RVD: the rightward distance between the vertex CV and the point of intersection between the membership function and the axis of the universe of discourse, along said axis; CV: the position of the vertex in the UdD; whereas the maximum value of the degree of truth (max) is a known value for all membership functions. These parameters are shown in figure 18. The dimensions, in bits, of the three parameters are a function of the number of membership functions to be described; it is recommended that CV have a number of bits that is equal to the number of bits that discretizes the UdD and, in order to have a large number of membership functions, that LVD and RVD have a greater or equal number of bits. In order to describe degenerate membership functions without increasing the number of bits of the above mentioned parameters, the following conventions are set: a) if LVD or RVD is equal to 0, the plot of the membership function is horizontal in the corresponding left or right side; b) if LVD and RVD are both equal to zero, there are two cases: (i) if the number of bits that defines CV is greater than, or equal to, the number of bits that describes the degree of truth, then the value of CV yields directly the value of α, that is to say, the plot of the membership function is horizontal throughout the UdD with the degree of truth equal to the value indicated in CV; (ii) if the number of bits that defines CV is smaller than the number of bits that describes the degree of truth, then it is possible to proceed in two ways: the value of CV indicates whether one is dealing with the membership function that is entirely at the value max, if CV is different from zero, or with the function that is entirely at the value 0, if CV = 0; the value of CV gives the value of α, appropriately discretized; that is to say, the plot of the membership function is horizontal throughout the UdD with the degree of truth equal to the value indicated in CV multiplied by a discretization parameter calculated as the maximum degree of truth divided by the maximum value of CV. Assuming that CV has a number of bits equal to the number of bits that discretizes the UdD and that LVD and RVD have a greater or equal number of bits, in this particular case the membership functions that can be described are: (i) all membership functions of the triangular type with the vertex at the maximum value of the degree of truth, as shown in figure 19; (ii) all membership functions of the pentagonal type framed in the border of the UdD, with the restriction of the distance LVD and RVD as shown in figure 20; (iii) all membership functions that have a right-angled trapezoid-type shape with the distance restriction LVD and RVD as shown in figure 21; (iv) the degenerate membership functions defined by: a) if the number of bits that defines CV is greater than, or equal to, the number of bits that describes the degree of truth, then the value of CV directly yields the value of α; that is to say, the plot of the membership function is horizontal throughout the UdD with the degree of truth equal to the value indicated in CV, as shown in figure 22; b) if the number of bits that defines CV is smaller 5 than the number of bits that describes the degree of truth, then there are two cases: the value of CV indicates whether one is dealing with the membership function that is entirely at the value max (MF1), if CV is different from zero, or with the membership function that is entirely at the value zero (MF2), if CV = 0, as shown in figure 23; the value of CV yields the value of α, appropriately discretized; that is to say, the plot of the membership function is horizontal throughout the UdD with the degree of truth equal to the value indicated in the CV multiplied by a discretization parameter K, calculated as the maximum degree of truth divided by the maximum value of CV, as shown in figure 24. (v) all crisp membership functions; that is to say, the membership function that is always zero except in CV, which assumes the truth value max, as shown in figure 25. This crisp membership function is not represented by LVD = RVD = 0, as would seem more logical, since the membership function LVD = RVD = 1 behaves, for the calculation of the value of α, like the crisp function, and therefore it is used like a crisp function, whereas the coding LVD = 0 or RVD = 0 is given the meaning of a horizontal side. In the case of triangular membership functions, the value of α is calculated in a manner that is similar to the preceding cases, with the exception that in this case no comparison with HT is performed, since the value that is obtained is directly the value α. The formulae for calculating the value of α are given hereinafter, considering the above mentioned membership functions with the specification that, among the degenerate functions, only the one that is entirely at the maximum value max is taken and is indicated by LVD = RVD = 0 and any value of CV. With reference to figure 26, if I is the value of the input variable and r is the value max of the degree of truth, fuzzification of the input value is achieved by adopting the geometric proportions that occur between homologous sides of similar triangles. For I > CV, the following proportion holds as regards the similar right-angled triangles V-CV-RV and IP-I-RV: α : (CV + RVD - I) = r : RVD from which: α = [r * (CV + RVD - I)] / RVD in this case, the value α is directly obtained. The fuzzy rules that are computed involve both the value of α and the negation of α ( α ), which is calculated as the complemented value of α with respect to the maximum degree of truth, that is to say, α = r - α. In order to simplify the hardware, it is possible to directly calculate the negation ( α ) and then obtain the optional value α. With reference to the similar right-angled triangles V-CV-RV and V-P-IP, the following proportion is written: A : r = (I - CV) : RVD and the value of A is obtained therefrom: A = [r * (I - CV)] / RVD If A > r then α = r else α = A This last check is performed because the value A that is obtained is greater than r if the input I is greater than RV; in this case the value α is equal to the value max of the degree of truth, that is to say, r. The value of α is instead: α = r - α With reference to figure 27, the same reasoning is applied to the left for I ≤ CV by using LVD. As regards the similar right-angled triangles V-CV-LV and IP-IL-V, it is possible to write the following proportion: α : (I - (CV - LVD)) = r : LVD from which: α = [r * (I - (CV - LVD))] / LVD In this case, the value α is directly obtained. Instead, since it is useful to calculate the value of the negation of α ( α ) as well, the following proportion is written with reference to the similar right-angled triangles V-CV-LV and V-P-IP: A: r = (CV - I) : LVD from which: A = [r * (CV - I)] / LVD If A > r then α = r else α = A. According to the above statements, the calculation of the negation of α is performed with the following formulae: If the input I is smaller than, or equal to, CV: A = [r * (CV - I)] / LVD if A > r then α = r else α = A. If the input I is greater than CV: A = [r * (I - CV)] / RVD if A > r then α = r else α = A. These two formulae entail performing one subtraction, one multiplication, and one division inside the chip. It should also be noted that once the membership function has been set, the values CV, LVD, and RVD are set, whereas the input value I varies. In this case, too, in order to further simplify and reduce the hardware area assigned to the calculation of the value of α, an additional refinement has been introduced that allows us to replace the hardware divider with an arithmetic shift. Assuming that the maximum value of LVD and RVD is LVD = RVD = 2 n - 1, the following constants are introduced: KL = (r * 2 n ) / LVD KR = (r * 2 n ) / RVD and the following values are memorized as significant parameters of the membership function: KL, CV, KR Therefore the formulae for calculating the value of α are: If the input I is smaller than, or equal to, CV: A = [KL * (CV - I)] / 2 n if A > r then α = r else α = A; If the input I is greater than CV: A = [KR * (I - CV)] / 2 n if A > r then α = r else α = A. Accordingly, the chip contains only one subtracter and one multiplier, whereas the division has been replaced by a rightward logic shift of n bits which, in hardware, is performed simply by truncating the n less significant bits of the output bus of the multiplier. It is specified that the division is eliminated inside the chip by virtue of the introduction of the constants KL and KR, whereas the parameters that identify the membership function are LVD, CV, and RVD. In the calculation of KL or KR, which is performed in software during compiling, multiplication by 2 n is performed so as to avoid losing precision in the division of integers by LVD or RVD which, during compiling is always performed as a division by integers with over- or under-approximation. The flowchart of the method according to the present invention based on these last formulae is shown in figure 28. Initially, the block 38 tests the condition I > CV. If the result of this test is positive, the block 39 assigns the values of formula (14) to the parameters A, B, and D. If instead the condition I > CV is not met, the values of formula (13) are assigned to the parameters A, B, and D by the block 40. The block 41 then calculates the value C = (D * (A - B)), which is a part of the generic formula of formulae (13) and (14). The value C is then sent to the block 42, which first of all removes the n less significant bits and then adds the most significant bit of the n truncated bits to the result. This produces the value of A, which is compared in the condition A > r by means of the block 43. If the result of the comparison is positive, the value of r is assigned to the value of the negation of α ( α ) by means of the block 44. If instead the condition A > r is not met, the block 45 assigns the value of A to the value of α . Figure 29 illustrates the circuital embodiment of the method shown in figure 28. As in the previous circuits, the input variable I is memorized in the register 8, whereas the characteristic parameters of the membership function KL, CV, and KR are memorized respectively in the registers 9-11. The value r, which is equal for all the membership functions, is memorized in the register 50. The modulus comparator 14 tests the condition I > CV. On the basis of the result obtained, the values of formulae (13) or (14) are assigned to the parameters A, B, and D respectively by means of the multiplexers 15a-c. The values A and B provided by the multiplexers 15a and 15b are subtracted from one another (A - B) by the subtracter 16. The result of this operation is sent to the multiplier 32, which multiplies the value (A - B) by the value of D, which is provided by the multiplexer 15c. At the output of the multiplier 32, the n less significant bits are eliminated and the most significant bit is added to the result by means of the adder 49. The output of the adder 49 is the value A of figures 26 and 27. This value is compared, by means of the comparator 51, with the value of r, which is provided by the register 50, in the condition A > r. On the basis of the result obtained, the multiplexer 52 gives the value of r or the value of A to the value of the negation of α ( α ). The subsequent calculation of α is obvious and can be performed with the formula α = r - α . From the above description it is evident that the present invention fully achieves the intended aim and objects. The invention thus conceived is susceptible of numerous modifications and variations, all of which are within the scope of the invention as claimed. Of course, it is possible to develop a corresponding circuit and flowchart for an embodiment of the method according to the present invention defined by formulae (9) and (11), that is to say, a non-improved version of the method and circuit of figures 28 and 29. Finally, all the details may be replaced with other technically equivalent ones. In practice, the materials employed, as well as the shapes and dimensions, may be any according to the requirements without thereby abandoning the scope of the protection of the appended claims. Where technical features mentioned in any claim are followed by reference signs, those reference signs have been included for the sole purpose of increasing the intelligibility of the claims and accordingly such reference signs do not have any limiting effect on the interpretation of each element identified by way of example by such reference signs.

Claims: A method for memorizing membership functions (MF), comprising the step of: memorizing the position of the vertex (CV) of the triangle that defines the function in the universe of discourse (UdD); characterized in that it further comprises the steps of: memorizing a first distance (LVD) between said position of the vertex in the universe of discourse and the point of intersection between the left side of the triangle that defines the membership function and the axis of the universe of discourse; memorizing a second distance (RVD) between said position of the vertex in the universe of discourse and the point of intersection between the right side of the triangle that defines the membership function and the axis of the universe of discourse; memorizing the altitude (HV) of said triangle that defines the membership function (MF); and memorizing the altitude (HT) of the trapezoid defined by said triangle that defines the membership function. The method according to claim 1, characterized in that the maximum value of the degree of truth is equal for all the membership functions. The method according to claim 1, characterized in that for a crisp membership function and for functions having vertical sides, said first distance (LVD) and said second distance (RVD) are equal to a number that is relatively small with respect to the number of the bits that discretize the universe of discourse. The method according to claim 1, characterized in that for a horizontal membership function said first distance (LVD) and said second distance (RVD) are equal to zero. The method according to claim 1, characterized in that it further comprises the following steps: memorizing a first parameter (KL), defined by multiplying the maximum degree of truth by 2 n and by dividing, with approximation, by said first distance (LVD); and memorizing a second parameter (KR), defined by multiplying the maximum degree of truth by 2 n and by dividing, with approximation, by said second distance (RVD); the value of n being the bit value with which said first and second distances are defined.

Inventors: DI BELLA DARIO, MATRANGA VINCENZO, PAPPALARDO FRANCESCO, TESI DAVIDE, DI BELLA, DARIO, MATRANGA, VINCENZO, PAPPALARDO, FRANCESCO, TESI, DAVIDE
Applicants: CONS RIC MICROELETTRONICA, CONSORZIO PER LA RICERCA SULLA
Application year: 1995
Main classification: G06F7
IPC codes: G06N7, G06F7, G06F9