Approximating Formulas in the Frame of Basic Logic
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UNIVERSITY OF OSTRAVA FACULTY OF SCIENCE DEPARTMENT OF MATHEMATICS APPROXIMATING FORMULAS IN THE FRAME OF BASIC LOGIC Ph.D. THESIS AUTHOR: Martina Daˇnkov´a SUPERVISOR: Irina Perfilieva 2005 OSTRAVSKA´ UNIVERZITA V OSTRAVEˇ PRˇ´IRODOVEDECKˇ A´ FAKULTA KATEDRA MATEMATIKY APROXIMACNˇ ´I FORMULE V RAMCI´ BASIC LOGIKY. DOKTORSKA´ DISERTACNˇ ´I PRACE´ AUTOR: Martina Daˇnkov´a VEDOUC´I PRACE:´ Irina Perfilieva 2005 Prohlaˇsuji, ˇze pˇredloˇzen´apr´ace je m´ym p˚uvodn´ım autorsk´ym d´ılem, kter´ejsem vypracoval samostatnˇe. Veˇskerou literaturu a dalˇs´ızdroje, z nichˇzjsem pˇri zpra- cov´an´ıˇcerpal, v pr´aci ˇr´adnˇecituji a jsou uvedeny v seznamu pouˇzit´eliteratury. Ostrava...................... ....................... .... (podpis) Beru na vˇedom´ı, ˇze tato doktorsk´adisertaˇcn´ıpr´ace je majetkem Ostravsk´euniverzity (autorsk´yz´akon C.ˇ 121/2000 Sb., §60 odst. 1), bez jej´ıho souhlasu nesm´ıb´yt nic z obsahu pr´ace publikov´ano. Souhlas´ım s prezenˇcn´ım zpˇr´ıstupnˇen´ım sv´epr´ace v Univerzitn´ıknihovnˇeOstravsk´e univerzity. Ostrava...................... ....................... .... (podpis) “It has long been an axiom of mine that the little things are infinitely the most important.” Sherlock Holmes 5 Summary An interesting class of decision making systems is that which is based on lin- guistic descriptions, i.e. finite sets of IF-THEN rules describing a process, situation, environment etc. These rules often include natural language expressions character- ized by a position on an ordered scale, usually on a real interval. As typical examples of such expressions, let us name light, loud, approximately 5, smaller then 7, warmer then 78 etc. The main task is to derive a conclusion from an observation on the basis of linguistic description by means of some feasible inference. This thesis proposes a methodology for dealing with the above-described situa- tion and studies its properties. The background for it is fuzzy predicate calculus. Considered fuzzy IF-THEN rules are understood in two different ways, i.e. as lin- guistically expressed logical conjunctions or implications. We strictly distinguish the syntactic from the semantic level of study. First, we introduce a generalized notion of normal forms aimed at formalizing linguistically expressed collection of fuzzy IF-THEN rules and prove their represen- tation abilities for a special class of formulas. Special inference rules determining conclusion from the observation are proposed as well. Moreover, the properties of such obtained conclusion are studied. Later on, we characterize this special class of formulas from the functional point of view. This leads to the general result relating to an approximations by functions representing normal forms. Finally, we apply all these result and present the complete automated learning method which conceives the rule-base composed of a set of IF-THEN rules from data assemble from a real process or environment. Keywords: Normal forms, Approximate inference, IF-THEN rules, Fuzzy logic, Fuzzy relations, Approximation, Fuzzy Systems, Genetic Algorithms. 6 Anotace V´yznamnou tˇr´ıdu rozhodovac´ıch syst´em˚utvoˇr´ısyst´emy zaloˇzen´ena jazykov´em popisu, tj. na koneˇcn´emnoˇzinˇepravidel typu JESTLIZE-PAKˇ popisuj´ıc´ıch proces, situaci, syst´emov´eprostˇred´ıap. Tato pravidla ˇcasto obsahuj´ıv´yrazy pˇrirozen´eho jazyka charakteristick´esvou pozic´ına uspoˇr´adan´eˇsk´ale, vˇetˇsinou na re´aln´em inter- valu. Typick´ym pˇr´ıkladem jsou v´yrazy jako lehk´y, hlasit´y, pˇribliˇznˇe5, m´enˇeneˇz7, teplejˇs´ıneˇz78 oC atd. Z´akladn´ım ´ukolem je odvodit z´avˇer z urˇcit´eho pozorov´an´ı na z´akladˇedan´eho jazykov´eho popisu prostˇrednictv´ım vhodn´einference. C´ılem t´eto disertace je navrhnout urˇcitou metodologii pro ˇreˇsen´ı situac´ı uve- den´ych v´yˇsea prozkoumat jej´ıvlastnosti. Tato metodologie je zaloˇzena na kalkulu predik´atov´efuzzy logiky. Uvaˇzovan´apravidla typu JESTLIZE-PAKˇ ch´apeme dvˇema odliˇsn´ymi zp˚usoby - bud’ jako jazykovˇevyj´adˇren´elogick´ekonjunkce, nebo implikace. V cel´epr´aci pˇr´ısnˇerozliˇsujeme syntaktickou a s´emantickou str´anku probl´emu. K tomuto ´uˇcelu nejprve zavedeme zobecnˇen´ypojem norm´aln´ıformy vhodn´ypro formalizaci jazykovˇevyj´adˇren´ych soubor˚upravidel typu JESTLIZE-PAKˇ a uk´aˇzeme, nakolik umoˇzˇnuje reprezentovat formule z urˇcit´etˇr´ıdy. Navrhneme rovnˇeˇzspeci´aln´ı odvozovac´ıpravidla pro stanoven´ız´avˇer˚uz pozorov´an´ıa prozkoum´ame vlastnosti takto z´ıskan´ych z´avˇer˚u. V dalˇs´ıˇc´asti pod´ame funkcion´aln´ıcharakterizaci uveden´etˇr´ıdy formul´ı, jeˇzvede k obecn´emu v´ysledku o aproximac´ıch funkcemi reprezentuj´ıc´ımi norm´aln´ıformy. Na z´avˇer provedeme aplikaci tˇechto v´ysledk˚ua pˇredvedeme metodu plnˇeauto- matick´eho uˇcen´ı, jeˇzvytv´aˇr´ıb´azi pravidel sloˇzenou z pravidel typu JESTLIZE-PAKˇ podle dat z´ıskan´ych z re´aln´ych proces˚uˇci prostˇred´ı. Kl´ıˇcov´aslova: Norm´aln´ı formy, Pˇribliˇzn´adedukce, JESTLIZE-PAKˇ pravidla, Fuzzy logika, Fuzzy relace, Aproximace, Fuzzy syst´emy, Genetick´ealgoritmy. 7 Preface Fuzzy logics are intend to handle information affected by inexactness or in the other words incorporate the vague phenomenon. Enormous number of applications in expert systems verify the role of fuzzy logic as an important tool compiling expert knowledge into a rule-base approximately describing a real system in a natural way. This is done using so called fuzzy IF-THEN rules. Formalizing these rules into the language of logic leads to formulas having special form commensurate with interpretation of available knowledge. The first type of fuzzy rules reflects a functional dependency, e.g. monotone dependency can be read as follows ”X is small” AND ”Y is small” OR ”X is medium” AND ”Y is medium” OR ”X is big” AND ”Y is big” Such interpretation can be naturally formalized as a disjunctive normal form. A different type of fuzzy rules is that which forms conjunctive normal form from the logical point of view. This type of fuzzy rules can be regarded as summarizing an expert knowledge. Having on mind the same dependency as above, we can read it as IF ”X is small” THEN ”Y is small” AND IF ”X is medium” THEN ”Y is medium” AND IF ”X is big” THEN ”Y is big” Generally, we may speak about the same system or process but we describe it in 8 two different ways with distinct functional realization. On the other side, we expect that both descriptions are at least partially equivalent. The aim of this thesis is to propose generalized normal forms suitable to formalize a collection of fuzzy IF-THEN rules, which satisfy the natural requirement for partial equivalence and further investigate their relationship. Furthermore, the significant task comprises of introducing a new automated learning mechanism able to find the normal forms on the basis of an available information. The results together with the whole methodology from this thesis will be used in the software system LFLC 2000 developed on Institute for Research and Applications of Fuzzy Modeling. This software system allows to design, test and apply linguistic descriptions, and additionally, it is endowed with automated learning procedures. It already proved itself to be useful in practical applications. I want to express my gratitude to my supervisor Prof. Irina Perfilieva for her support, valuable comments and permanent encouragement which made it possible to finish this thesis. Moreover, I would like to thank my boss Prof. Vil´em Nov´ak for working conditions he created in Institute for Research and Applications of Fuzzy Modeling. The warm thanks goes to Libor Bˇehounek and Anton´ın Dvoˇr´ak for the watchful reading throughout this thesis. I would also like to thank colleagues from our Institute making the friendly atmosphere. Last but not least, I thank my parents for warm atmosphere which gave me strength in difficult times. Ostrava, May 2005 Martina Daˇnkov´a 9 Table of Contents Summary 6 Anotace 7 Preface 8 Table of Contents 10 List of Figures 13 List of Symbols 15 1 Introduction 16 1.1 Fuzzy Logic in the modern scientific world . 16 1.2 NormalformsandfuzzyIF-THENrules . 17 1.3 GeneralizationofNormalforms . 18 1.4 Theoutlineofthiswork . 20 2 Preliminaries 22 2.1 Fuzzysetsandfuzzyrelations . 22 2.1.1 Basicnotions ........................... 22 2.1.2 T-norms and their residua as logical operations . 23 2.1.3 Extensional fuzzy relations . 28 2.2 Basicfuzzypredicatelogic . 34 2.2.1 Algebraic structures for BL and BL∀ .............. 35 2.2.2 Syntaxandsemantics. 37 2.2.3 Provable propositional and predicate tautologies . ..... 41 2.2.4 Normal Forms in BL∀ ...................... 43 10 3 Logical approximation 48 3.1 Normal Forms for Extensional Formulas . 50 3.1.1 Characterization of extensional formulas . 52 3.1.2 Logical Approximation by Finite Normal Forms . 56 3.2 Logical approximation in approximate inference . ...... 65 3.2.1 Approximate inferences based on Normal Forms . 67 3.2.2 Rules of inference and the theory of fuzzy control . 71 3.2.3 Properties of approximate inferences . 73 3.2.4 Consequences to fuzzy control . 76 4 Approximations over linearly ordered BL-algebras 83 4.1 Normal Forms for extensional fuzzy relations . 84 4.1.1 Approximating abilities of Normal Forms . 85 4.1.2 Representation of extensional fuzzy relations . 87 4.1.3 Illustrations ............................ 88 4.2 Functions associated with formulas of BL-logic . 88 4.2.1 Normal Forms for functions represented byLukasiewicz formulas 91 4.2.2 Functions associated with Product (Goguen) formulas . 94 4.2.3 Functions associated with G¨odel (Minimum) formulas . 100 4.2.4 Normal forms for functions associated with formulas of BL-logic106 4.3 Characterization of extensional fuzzy relations . .......108 4.3.1 Extensionality w.r.t. Reflexivity means Lipschitz Continuity . 110 4.3.2 From Lipschitz Continuity to Extensionality w.r.t. Reflexivity 114 4.3.3 Notes on ∧-extensionality . .124 4.3.4 Approximation Theorem . 125 5 Genetic Algorithm + Logical Approximation = Optimized Rule-Base 128 5.1 IntroductiontoGeneticAlgorithms .