2. Efficient Decomposition of Large Fuzzy Functions and Relations Marek Perkowski + Portland State University, Dept. Electrical Engineering, Portland, Oregon 97207, Tel. 503-725-5411, Fax (503) 725- 4882, USA FUZZY LOGIC IN ROBOTICS

3. Minimization of Fuzzy Functions Fuzzy functions are realized in: –analog hardware –software Why to minimize fuzzy logic functions? –Smaller area –Lower Power –Simpler and faster program

4. Minimization Approaches to Fuzzy Functions Two level minimization (Siy,Kandel, Mukaidono, Lee, Rovatti et al) Algebraic factorization (Wielgus) Genetic algorithms (Thrift, Bonarini, many authors) Fuzzy decision diagrams (Moraga, Perkowski) Functional decomposition (Kandel, Kandel and Francioni)

5. Graphical Representations Fuzzy Maps Lattice of two variables The Subsumption rule Kandel’s Form to Decompose Fuzzy Functions

6. Identities The identities for fuzzy algebra are: Idempotency: X + X = X, X * X = X Commutativity: X + Y = Y + X, X * Y = Y * X Associativity: (X + Y) + Z = X + (Y + Z), (X * Y) * Z = X * (Y* Z) Absorption: X + (X * Y) = X, X * (X + Y) = X Distributivity: X + (Y * Z) = (X + Y) * (X + Z), X * (Y + Z) = (X * Y) + (X * Z) Complement: X’’ = X DeMorgan's Laws: (X + Y)’ = X’ * Y’, (X * Y)’ = X’ + Y’

7. Transformations Some transformations of fuzzy sets with examples follow: x’b + xb = (x + x’)b  b xb + xx’b = xb(1 + x’) = xb x’b + xx’b = x’b(1 + x) = x’b a + xa = a(1 + x) = a a + x’a = a(1 + x’) = a a + xx’a = a a + 0 = a x + 0 = x x * 0 = 0 x + 1 = 1 x * 1 = xExamples: a + xa + x’b + xx’b = a(1 + x) + x’b(1 + x) = a + x’b a + xa + x’a + xx’a = a(1 + x + x’ + xx’) = a

8. Differences Between Boolean Logic and Fuzzy Logic Boolean logic the value of a variable and its inverse are always disjoint (X * X’ = 0) and (X + X’ = 1) because the values are either zero or one. Fuzzy logic membership functions can be either disjoint or non-disjoint. Example of a fuzzy non-linear and linear membership function X is shown (a) with its inverse membership function shown in (b).

9. Fuzzy Intersection and Union From the membership functions shown in the top in (a), and complement X’ (b) the intersection of fuzzy variable X and its complement X’ is shown bottom in (a). From the membership functions shown in the top in (a), and complement X’ (b) the union of fuzzy variable X and its complement X’ is shown bottom in (b).

10. Fuzzy map may be regarded as an extension of the Veitch diagram, which forms also the basis for the Karnauph map. The functions shown in (a) and (b) are equivalent to f(x 1, x 2 ) = x’ 1 x 2 + x 1 x’ 1 x’ 2 = x 1 x’ 1 Fuzzy Maps (b) f(x 1, x 2 ) = x 1 x’ 1 (a) f(x 1, x 2 ) =x 1 x’ 1 x 2 + x 1 x’ 1 x’ 2

11. Lattice of Two Variables Shows the relationship of all the possible terms. Shows which two terms can be reduced to a single term.

12.  x i x’ I  +  ’ x i x’ I  = x i x’ I   x i x’ I  +  ’ x i x’ I  = x i x’ I  The Subsumption Rule Used to reduce a fuzzy logic function. Operations on two variable map are shown with I subsuming i.

13. Form Needed to Decompose Fuzzy Functions Form requirements: 1.Sum-of-products 2.Canonical Figures show the function x 2 x’ 2 + x’ 1 x 2 + x 1 x’ 2 + x 1 x’ 1 x’ 2 before using the subsuming rules in (a) and after in (d) x’ 1 x 2 + x 1 x’ 2. x1

14. x 1 x’ 2 + x 1 x’ 1 x’ 2 x 2 x’ 2 + x’ 1 x 2 + x 1 x’ 2 + x 1 x’ 1 x’ 2 x 1 x’ 2 = x 2 x’ 2 + x’ 1 x 2 + x 1 x’ 2 (1+ x’ 1 ) x 1 x’ 2 = x 2 x’ 2 + x’ 1 x 2 + x 1 x’ 2 x 1 x’ 2 = x’ 1 x 2 + x 1 x’ 2.  x i x’ I  +  ’ x i x’ I  = x i x’ I   x i x’ I  +  ’ x i x’ I  = x i x’ I  subsumption Let us use subsumption to verify:

15. Kandel's and Francioni's Approach Decomposition Implicant Pattern (DIP) Variable Matching DIP’s Table S-Maps Example using Kandel and Francioni approach

16. Decomposition Implicant Pattern (DIP)

17. Variable Matching DIPs Table h(X)h’(X)DIP x i x i x j x’ i x’ j x’ i x j x i x’ j x i x j + x’ i x’ j x’ i x’ i  x’ j x i  x j x i + x’ j x’ i + x j x i x’ j + x’ i x j -12345-12345 Tabular form of Decomposition Implicant Pattern (DIP) used in Kandel’s and Francioni’s approach

18. S-Maps Arrange two-variable fuzzy maps for n variables. This method is just done by iteration to form an n variable S-map. This shows X 1 is made up of repeated X 2 and X 3 two variable maps.

19. Example using Kandel and Francioni approach f = x’y’zz’ + xz + w’x’zz’ + wyz From DIP 1 implies: g(w,y) =wy, G’ (w,y) = w’ + y’ f = (wy)z + (w’ + y’) x’zz’ + xz By substituting: G(w,y) = G(Y) and G’(w,y) = G’(Y) f = F[(G(w,y), x,z)] = G(Y)z + G’(Y) x’zz’ + xz

20. APPROACHES TO FUZZY LOGIC DECOMPOSITION Kandel's and Francioni's Approach based on graphical representations: –non-algorithmic –not scalable to larger functions –no software Fuzzy to Multiple-valued Function Conversion Approach and use of Generalized Ashenhurst- Curtis Decomposition Our new approach

21. New Approach: Fuzzy to Multiple- valued Function Conversion and A/C Decomposition Fuzzy Function Ternary Map Fuzzy Function to Three-valued Function Conversion Example –The MAX operation forms the result –The result from canonical form are the same as from the non-canonical form –Thus time consuming reduction to canonical form is not necessary

22. Fuzzy Function Ternary Map This shows the mapping between the fuzzy terms and terms in the ternary map.

23. Conversion Fuzzy Function to Three-valued Function Conversion Example Conversion of the Fuzzy function terms: x 2 x’ 2 x’ 1 x 2 x 1 x’ 2 x 1 x’ 1 x’ 2 In non- canonical form using the MIN operation as shown for f = x 2 x’ 2 +x’ 1 x 2 +x 1 x’ 2 + x 1 x’ 1 x’ 2 x 2 x’ 2 x’ 1 x 2 x 1 x’ 2 x 1 x’ 1 x’ 2 Non-canonical

24. The MAX operation form the result Combining the three-valued term functions into a single three-valued function is performed using the MAX Operation

25. The result from the canonical form is the same as from the non-canonical form F = x 2 x’ 2 +x’ 1 x 2 +x 1 x’ 2 + x 1 x’ 1 x’ 2 conversion is equal to F(x 1 x 2 ) =x’ 1 x 2 +x 1 x’ 2 canonical Non-canonical

26. Fuzzy to Multiple-valued Function Conversion Approach Example –Fuzzy to Multiple-valued Function Conversion Example Fuzzy function to Multiple-valued function Input and results of decomposition Multiple-valued function to fuzzy function with circuit –Method of Doing More Examples Using Mathcad to do the MIN, MAX Operations –Fuzzy Function Decomposition Results

27. F(x,y,z) = xz + x’y’zz’ + yz Entire flow of our method Initial non-canonical expression Decomposed Function Generalized Ashenhurst- Curtis Decomposition Fuzzy to Ternary Conversion decomposed expression H(x,y) = Gz + zz’. G(x,y) = x+y

28. F(x,y,z) = xz + x’y’zz’ + yz Entire flow of our method Initial non-canonical expression Decomposition is based on finding patterns in this table 0 1 0 0 1 1 0 1 2 Only three patterns

29. Multiple-valued function to fuzzy function with circuit Two solutions are obtained, G(x,y) = x+y, H(x,y) = Gz+zz’ Fuzzy terms Gz, G’zz’ and zz’ of H are shown. G(x,y) = x+y, H(x,y) = Gz+G’zz’

30. Contents Fuzzy logic Fuzzy logic systems applications Approaches to fuzzy logic decomposition  Decomposition program Conclusion

31. Generalization of the Ashenhurst- Curtis decomposition model

32. Short Introduction: multiple-valued logic {0,1} - binary logic (a special case) {0,1,2} - a ternary logic {0,1,2,3} - a quaternary logic, etc Signals can have values from some set, for instance {0,1,2}, or {0,1,2,3} Minimal value MINMIN MAXMAX 2 Maximal value 1 2 1 2 3 3 3

34. Decomposition is hierarchical At every step many decompositions exist

35. Functional Decomposition Evaluates the data function and attempts to decompose into simpler functions. if A  B = , it is disjoint decomposition if A  B  , it is non-disjoint decomposition X B - bound set A - free set F(X) = H( G(B), A ), X = A  B

36. A Standard Map of function ‘z’ Bound Set Free Set a b \ c z Columns 0 and 1 and columns 0 and 2 are compatible column compatibility = 2

37. Decomposition of Multi-Valued Relations if A  B = , it is disjoint decomposition if A  B  , it is non-disjoint decomposition F(X) = H( G(B), A ), X = A  B Relation A B X

38. Forming a CCG from a K-Map z Bound Set Free Set a b \ c Columns 0 and 1 and columns 0 and 2 are compatible column compatibility index = 2 C1C1 C2C2 C0C0 Column Compatibility Graph

39. Forming a CIG from a K-Map Columns 1 and 2 are incompatible chromatic number = 2 z a b \ c C1C1 C2C2 C0C0 Column Incompatibility Graph

40. CCG and CIG are complementary C1C1 C2C2 C0C0 C1C1 C2C2 C0C0 Column Compatibility Graph Column Incompatibility Graph Maximal clique covering clique partitioning Graph coloring graph multi- coloring

41. clique partitioning example.

42. Maximal clique covering example.

43. G \ c g = a high pass filter whose acceptance threshold begins at c > 1 Map of relation G G \ c From CIG After induction

44. Cost Function Decomposed Function Cardinality is the total cost of all blocks. Cost is defined for a single block in terms of the block’s n inputs and m outputs Cost := m * 2 n

45. Example of DFC calculation B1 B2 B3 Cost(B3) =2 2 *1=4 Cost(B1) =2 4 *1=16 Cost(B2) =2 3 *2=16 Total DFC = 16 + 16 + 4 = 36 Other cost functions

46. Decomposition Algorithm Find a set of partitions (A i, B i ) of input variables (X) into free variables (A) and bound variables (B) For each partitioning, find decomposition F(X) = H i (G i (B i ), A i ) such that column multiplicity is minimal, and calculate DFC Repeat the process for all partitioning until the decomposition with minimum DFC is found.

47. Algorithm Requirements Since the process is iterative, it is of high importance that minimization of the column multiplicity index is done as fast as possible. absolute minimumAt the same time, for a given partitioning, it is important that the value of the column multiplicity is as close to the absolute minimum value

48. Column Multiplicity 3 2 1 4 Bound Set Free Set

49. Column Multiplicity-other example 3 2 1 4 Bound Set 1 2 3 4 Free Set AB CD D C 0 1 0 1 0 0 1 1 X=G(C,D) X=C in this case But how to calculate function H?

50. relation Decomposition of multiple-valued relation Karnaugh Map Compatibility Graph for columns compatible Kmap of block G Kmap of block H One level of decomposition

51. Compatibility Checking and Correction for Relations Example Function that needs checked and corrected shown in a decomposition- map.

52. Compatibility Graph Show Cliques Cliques before checking and correction: clique 0 = 0 1 2 clique 1 = 0 3 Cliques after: clique 0 = 0 clique 1 = 0 3 clique 2 = 1 2 Compatibility graph and corrected cliques shown left

53. Discovering new concepts purchasing a carDiscovering concepts useful for purchasing a car

54. Variable ordering

55. Vacuous variables removing Variables b and d reduce uncertainty o f y to 0 which means they provide all the information necessary for determination of the output y Variables a and c are vacuous

56. Example of removing inessential variables (a) original function (b) variable a removed (c) variable b removed, variable c is no longer inessential.

57. Need to Decompose Multiple- valued Functions and Relations Multiple-valued and Inconsistent Data Ways to Create Relations –Decomposition Process to Create Relations –Program to Change Inconsistency data into Relations

58. Decomposition Structure General flow chart of RMVGUD Program.

59. Results of Relation Decomposition 3 21 18 68 157 244 132 323 1066 4 10 hayes flare 1 flare 2 No. of Blocks No. of cubes No. of Cubes No. of Inputs Output FileInput FileFile

60. CONCLUSION Fuzzy functions are efficiently realized as multi-level networks of fuzzy operators The approach has been extended also to relations. Relation is decomposed to network of relations and functions, non- decomposable relations are realized using other methods as the simplest functions Advantages of the new approach to fuzzy logic decomposition –Eliminates the need for time-consuming conversion to canonical form –Eliminates the use of S-maps –Entirely algorithmic –Enables decomposition of large fuzzy expressions –Software tool exists