Soft approach to intelligent systems and soft computing : Soft approach to intelligent systems and soft computing Vesa A. Niskanen University of Helsinki


Contents: Contents Intelligent systems Soft computing Application areas


Traditions in human sciences: Traditions in human sciences


Intelligent systems: Intelligent systems Intelligence: System must perform meaningful operations. Interprets information. Comprehends the relations between the phenomena or objects. Applies the acquired information to new conditions.


Objectives of intelligent systems: Objectives of intelligent systems Everyday routines of human beings: vision, language processing, common sense reasoning, learning, robotics. Artificial routines: games, mathematics, logic, programming. Expert systems (ES)


Traditional approaches: Traditional approaches Mathematical models: Black boxes, number crunching. Rule-based systems (crisp & bivalent): Large rule bases.


Soft computing (SC): Soft computing (SC) Objective: Mimic human (linguistic) reasoning Main constituents: - Fuzzy systems - Neural networks - Evolutionary computing - Probabilistic reasoning


Constituents of SC: Constituents of SC Fuzzy systems => imprecision Neural networks => learning Probabilistic reasoning => uncertainty Evolutionary computing => optimization Over 24 000 publications today


SC: a user-friendly interface : SC: a user-friendly interface


Advantages of SC: Advantages of SC Models base on human reasoning. Models can be - linguistic - simple (no number crunching), - comprehensible (no black boxes), - fast when computing, - good in practice.


SC today (Zadeh): SC today (Zadeh) Computing with words (CW) Theory of information granulation (TFIG) Computational theory of perceptions (CTP)


Possible SC data & operations: Possible SC data & operations Numeric data: 5, about 5, 5 to 6, about 5 to 6 Linguistic data: cheap, very big, not high, medium or bad Functions & relations: f(x), about f(x), fairly similar, much greater


Neural networks (NN, 1940's): Neural networks (NN, 1940's) Neural networks offer a powerful method to explore, classify, and identify patterns in data. Website of Matlab Neuron: y=wixi


Machine learning (supervised): Machine learning (supervised) Pattern recognition based on training data. Classification supervised by instructor. Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. Peach Plum ? Instructor


Machine learning (unsupervised): Machine learning (unsupervised) Pattern recognition based on training data. Classification based on structure of data (clustering). Neural (crisp or fuzzy), neuro-fuzzy and fuzzy models. Peach Plum Nectarine Labeling


Machine learning (unsupervised): Machine learning (unsupervised) Self-organized maps (Kohonen). Fuzzy c-means (Bezdek). Subclust (Yager, Chiu). Peach Plum Nectarine Labeling Websom Self-Organizing Maps for Internet Exploration


Fuzzy systems (Zadeh, 1960's): Fuzzy systems (Zadeh, 1960's) Deal with imprecise entities in automated environments (computer environments) Base on fuzzy set theory and fuzzy logic. Most applications in control and decision making Omron’s fuzzy processor Omron Electronics Matlab's Fuzzy Logic Toolbox


SC applications: control: SC applications: control Heavy industry (Matsushita, Siemens, Stora-Enso) Home appliances (Canon, Sony, Goldstar, Siemens) Automobiles (Nissan, Mitsubishi, Daimler-Chrysler, BMW, Volkswagen) Spacecrafts (NASA)


SC applications: business: SC applications: business hospital stay prediction, TV commercial slot evaluation, address matching, fuzzy cluster analysis, sales prognosis for mail order house, multi-criteria optimization etc. (source: FuzzyTech) supplier evaluation for sample testing, customer targeting, sequencing, scheduling, optimizing R&D projects, knowledge-based prognosis, fuzzy data analysis


SC applications: finance: SC applications: finance Fuzzy scoring for mortgage applicants, creditworthiness assessment, fuzzy-enhanced score card for lease risk assessment, risk profile analysis, insurance fraud detection, cash supply optimization, foreign exchange trading, insider trading surveillance, investor classification etc. Source: FuzzyTech


SC applications: robotics: SC applications: robotics Fukuda’s lab Joseph F. Engelberger We are proud to announce that the HelpMate Robotic Courier has been acquired by Pyxis Corporation. Entertainment robot AIBO


SC applications: others: SC applications: others Statistics Social sciences Behavioural sciences Biology Medicine


(Neuro)-fuzzy system construction: (Neuro)-fuzzy system construction Training data Experts Fuzzy rules (SOM, c-means etc.) Control data System evaluation (errors) Tuning (NN) New system


Model construction (mathematical): Model construction (mathematical) Mathematical models are functions. Deep knowledge on mathematics. If non-linear (eg. NN), laborious calculations and computing. Linear models can be too simplified. How can we find appropriate functions? Y=1-1./(1 + EXP(-2*(X-5)))


Model construction (trad. rules ): Model construction (trad. rules ) If 0 Large rule bases. - Only one rule is fired for each input. - Coarse models.


Model construction (SC/fuzzy): Model construction (SC/fuzzy) If x0, then y1 If x5, then y0.5 If x10, then y0 - Approximate values - Rules only describe typical cases (no rule for each input). => Small rule bases. - A group of rules are partially fired simultaneously.


SC and future: SC and future SC and conventional methods should be used in combination.


Sources of SC: Sources of SC Books: www.springer.de/cgi-bin/search_book.pl?series=2941, www.springer.de/cgi-bin/search_book.pl?series=4240, www.elsevier.com/locate/fss, www.wkap.nl Others: Bisc Group Homepage of summer school


References: References J. Bezdek & S. Pal, Fuzzy models for pattern recognition (IEEE Press, New York, 1992). L. Zadeh, Fuzzy logic = Computing with words, IEEE Transactions on Fuzzy Systems, vol. 2, pp. 103-111, 1996. L. Zadeh, From Computing with Numbers to Computing with Words -- From Manipulation of Measurements to Manipulation of Perceptions, IEEE Transactions on Circuits and Systems, 45, 1999, 105-119. L. Zadeh, Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 90/2 (1997) 111-127. H.-J. Zimmermann, Fuzzy set theory and its applications (Kluwer, Dordrecht, 1991).