| 1 | #ifndef _NEUROIMPLFUZZY_H_ |
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| 2 | #define _NEUROIMPLFUZZY_H_ |
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| 3 | |
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| 4 | #include <stdlib.h> |
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| 5 | #include <math.h> |
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| 6 | |
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| 7 | #include "neuroimpl.h" |
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| 8 | #include "sstring.h" |
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| 9 | |
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| 10 | extern ParamEntry NI_FuzzyNeuro_tab[]; |
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| 11 | |
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| 12 | /** Does the fuzzyfication process (of inputs) and defuzzyfication proces (of outpurs) - represents fuzzy rules |
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| 13 | */ |
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| 14 | class NI_FuzzyNeuro : public NeuroImpl |
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| 15 | { |
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| 16 | private: |
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| 17 | |
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| 18 | double *fuzzySets; /// list of four digits which represents fuzzy sets: [0]-l, [1]-m, [2]-n, [3]-r, ... fuzzySet[4*i] = left, fuzzySet[4*i + 1] = midleft, fuzzySet[4*i + 2] = midright, fuzzySet[4*i + 3] = right |
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| 19 | |
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| 20 | /** Determines, which fuzzy set is connected with each input of neuron. For instance third rule: |
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| 21 | * 'IF input3 = fuzzy set #3 AND input5 = fuzzy set #1 then output2 = fuzzy set #6 AND output7 = fuzzy set #5' |
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| 22 | * the variables shoul have values as shown below: |
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| 23 | * RulesDef[4]=2; RulesDef[5]=2; //rule 3: 2 inputs, 2 outputs |
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| 24 | * Rules[2][0]=3, Rules[2][1]=3, Rules[2][2]=5, Rules[2][3]=1, Rules[2][4]=2, Rules[2][5]=6, Rules[2][6]=7, Rules[2][3]=5 |
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| 25 | */ |
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| 26 | int *rulesDef; ///list of rules definitions: nr of inputs in rule 1, nr of outputs in rule 1, ... and so on for each rule |
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| 27 | int **rules; ///list of rules body: input nr, fuzzy set nr, ... , output nr, fuzzy set nr, ... and so on for each rule |
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| 28 | |
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| 29 | /** |
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| 30 | * Sets defuzzyfication parameters: determines - for each rule - cut level <0;1> (minimum membership function of current rule). |
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| 31 | * In fact, defuzzParam remembers the values from 'first layer' - fuzzyfication layer (see neuron at documentation) |
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| 32 | * i.e. rule 1: defuzzParam[0] = 0.3522 |
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| 33 | */ |
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| 34 | double *defuzzParam; /// i.e.: defuzParam[5] = 0.455 means that rule #6 has got a minimum membership function (of given inputs set for this rule) at value 0.455 (it's cut level) |
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| 35 | |
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| 36 | protected: |
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| 37 | |
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| 38 | ///Fuzzy functions |
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| 39 | double TrapeziumFuzz(int which_fuzzy_set, double input_val); |
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| 40 | int Fuzzyfication(); |
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| 41 | int Defuzzyfication(); |
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| 42 | int GetFuzzySetParam(int set_nr, double &left, double &midleft, double &midright, double &right); |
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| 43 | |
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| 44 | public: |
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| 45 | |
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| 46 | int fuzzySetsNr; /// number of fuzzy sets |
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| 47 | int rulesNr; ///number of rules |
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| 48 | SString fuzzySetString; /// strings containing all fuzzy sets given in f0 |
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| 49 | SString fuzzyRulesString; /// strings containing all fuzzy rules given in f0 |
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| 50 | |
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| 51 | NI_FuzzyNeuro() {paramentries=NI_FuzzyNeuro_tab; fuzzySets=defuzzParam=NULL; rulesDef=NULL; rules=NULL;} |
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| 52 | ~NI_FuzzyNeuro(); |
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| 53 | NeuroImpl* makeNew() { return new NI_FuzzyNeuro(); }; |
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| 54 | void go(); |
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| 55 | int lateinit(); |
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| 56 | /** Function build model based on given genotype and conts number of neurons connected with fuzzy neuro, |
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| 57 | also checks number of fuzzy neuron inputs. |
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| 58 | \param genotype genotype to be scanned |
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| 59 | \param inputs number of fuzzy neuron inputs |
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| 60 | \param output number of fuzzy neuron outputs (= number of neurons connected to fuzzy neuron) |
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| 61 | @return success or failure |
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| 62 | **/ |
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| 63 | static int countOuts(const Model *m, const Neuro *fuzzy); |
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| 64 | |
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| 65 | }; |
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| 66 | |
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| 67 | #endif |
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