Bernd Reusch (Ed.) Computational Intelligence, Theory and Applications Advances in Soft Computing Editor-in-chief Prof Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul Newelska 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springer.com Kwang H Lee (Ed.) First Course on Fuzzy Theory and Applications, 2004 ISBN 3-540-22988-4 Miguel López-Díaz, Maria A Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz, Jonathan Lawry (Eds.) Soft Methodology and Random Information Systems, 2004 ISBN 3-540-22264-2 Ahmed Lotfi, Jonathon M Garibalbi (Eds.) Applications and Science in Soft Computing, 2004 ISBN 3-540-40856-8 Marek Kurzynski, Edward Puchala, Michal Wozniak, Andrzej Zolnierek (Eds.) Computer Recognition Systems, 2005 ISBN 3-540-25054-9 Abraham Ajith, Yasuhiko Dote, Takeshi Furuhashi, Mario Köppen, Azuma Ohuchi, Yukio Ohsawa (Eds.) Soft Computing as Transdisciplinary Science and Technology, 2005 ISBN 3-540-25055-7 Barbara Dunin-Keplicz, Andrzej Jankowski, Andrzej Skowron, Marcin Szczuka, (Eds.) Monitoring, Security, and Rescue Techniques in Multiagent Systems, 2005 ISBN 3-540-23245-1 Frank Hoffmann, Mario Köppen, Frank Klawonn, Rajkumar Roy (Eds.) Soft Computing Methodologies and Applications, 2005 ISBN 3-540-25726-8 Mieczyslaw A Klopotek, Slawomir T Wierzchon, Kryzysztof Trojanowski (Eds.) Intelligent Information Processing and Web Mining, 2005 ISBN 3-540-25056-5 Bernd Reusch, (Ed.) Computational Intelligence, Theory and Applications, 2005 ISBN 3-540-22807-1 Abraham Ajith, Bernard de Bacts, Mario Köppen, Bertram Nickolay (Eds.) Applied Soft Computing Technologies: The Challenge of Complexity, 2006 ISBN 3-540-31649-3 Mieczyslaw A Klopotek, Slawomir T Wierzchon, Kryzysztof Trojanowski (Eds.) Intelligent Information Processing and Web Mining, 2006 ISBN 3-540-33520-X Ashutosh Tiwari, Joshua Knowles, Eral Auineri, Keshav Dahal, Rajkumar Roy (Eds.) Applications and Soft Computing, 2006 ISBN 3-540-29123-7 Bernd Reusch (Ed.) Computational Intelligence, Theory and Applications International Conference 9th Fuzzy Days in Dortmund, Germany, Sept 18–20, 2006 Proceedings ABC Professor Dr Bernd Reusch University of Dortmund Computer Science I Otto-Hahn-Str i6 44227 Dortmund Germany E-mail: Bernd.Reusch@udo.edu Library of Congress Control Number: 2006930272 ISSN print edition: 1615-3871 ISSN electronic edition: 1860-0794 ISBN-10 3-540-34780-1 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34780-4 Springer Berlin Heidelberg New York This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable for prosecution under the German Copyright Law Springer is a part of Springer Science+Business Media springer.com c Springer-Verlag Berlin Heidelberg 2006 The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Typesetting: SPi Cover design: Erich Kirchner, Heidelberg Printed on acid-free paper SPIN: 11757382 89/SPi 543210 Preface For the 9th time since 1991 we invite researchers to participate in the Dortmund Fuzzy-Days I am very glad that our conference has established itself as an international forum for the discussion of new results in the filed of Computational Intelligence Again all papers had to undergo a thorough review: each one was judged by five referees to guarantee a solid quality of the programme From the beginning of the Fuzzy-Days on Lotfi A Zadeh felt associated with the conference I would like to express my gratitude for his encouragement and support and I am particularly glad that he once again delivers a keynote speech Much to my pleasure Ewa Orlowska, Radko Mesiar together with Vil´em Nov´ak, Ernesto Damiani together with Tharam Dillon and Nik Kasabov have also agreed to present new results of their work as keynote speakers Many thanks go to my friends Janusz Kacprzyk and Enric Trillas who together with Lotfi Zadeh again served as honorary chairmen Due to my retirement in 2006, these are the last Dortmund Fuzzy Days in the form we had developed over the years At this point I have to leave open, whether we find another forum or not I wish to thank all participants of the Dortmund Fuzzy-Days for their commitment to the conference and the organisers, namely Mrs Ulrike Lippe, for the excellent job they did Last but not least, I am obliged to the German research council for their valuable financial support September 2006 Bernd Reusch Programme Chairs Bernd Reusch Programme Committee Igor Aizenberg Ulrich Bodenhofer Arkady Borisov Bernadette Bouchon-Meunier Christer Carlsson Ernesto Damiani Bernhard De Baets Didier Dubois Francesc Esteva Mario Fedrizzi Janos Fodor Siegfried Gottwald Michel Grabisch Petr Hajek Aboul Ella Hassanien Kaoru Hirota Eyke Hă ullermeier Janusz Kacprzyk Nik Kasabov Gabriele Kern-Isberner Etienne E Kerre Jong-Hwan Kim Frank Klawonn Erich Peter Klement Vladik Kreinovich Rudolf Kruse Henrik Legind Larsen Stephan Lehmke Madjid Fathi Luis Magdalena Trevor Martin Radko Mesiar Claudio Moraga Vil´em Nov´ak Ewa Orlowska Endre Pap Witold Pedrycz Irina Perfilieva Olivier Pivert Susanne Saminger Elie Sanchez Hideo Tanaka Enric Trillas Peter Vojtas Michael Wagenknecht Takeshi Yamakawa Antonio di Nola Local Organization Wolfgang Hunscher Ulrike Lippe Thomas Wilke Contents Plenary Talk From Search Engines to Question-Answering Systems: The Problems of World Knowledge, Relevance, Deduction, and Precisiation Lotfi A Zadeh Invited Session: Fuzzy Multiperson and Multicriteria Decisions Modelling Session Organiser: Mario Fedrizzi A Fuzzy Approach to Optimal R&D Project Portfolio Selection Christer Carlsson, Robert Full´er, and P´eter Majlender Choquet Integration and Correlation Matrices in Fuzzy Inference Systems R.A Marques Pereira, P Serra, R.A Ribeiro 15 Linguistic Summarization of Some Static and Dynamic Features of Consensus Reaching Janusz Kacprzyk, Slawomir Zadro˙zny, and Anna Wilbik 19 Consistency for Nonadditive Measures: Analytical and Algebraic Methods Antonio Maturo, Massimo Squillante, and Aldo Ventre 29 VIII Contents Neural Nets Neuro-Fuzzy Kolmogorov’s Network with a Modified Perceptron Learning Rule for Classification Problems Vitaliy Kolodyazhniy, Yevgeniy Bodyanskiy, Valeriya Poyedyntseva, and Andreas Stephan 41 A Self-Tuning Controller for Teleoperation System using Evolutionary Learning Algorithms in Neural Networks Habib Allah Talavatifard, Kamran Razi, and Mohammad Bagher Menhaj 51 A Neural-Based Method for Choosing Embedding Dimension in Chaotic Time Series Analysis Sepideh J Rastin and Mohammad Bagher Menhaj 61 On Classification of Some Hopfield-Type Learning Rules via Stability Measures Mohammad Reza Rajati, Mohammad Bagher Menhaj 75 Applications I A New Genetic Based Algorithm for Channel Assignment Problems Seyed Alireza Ghasempour Shirazi and Mohammad Bagher Menhaj 85 Max-Product Fuzzy Relational Equations as Inference Engine for Prediction of Textile Yarn Properties Yordan Kyosev, Ketty Peeva, Ingo Reinbach, and Thomas Gries 93 Automatic Defects Classication and Feature Extraction Optimization Bernd Kuhlenkă otter, Carsten Krewet, and Xiang Zhang 105 Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine Ganyun LV, Xiaodong Wang and Yuanyuan Jin 117 Plenary Talk Fifteen Years of Fuzzy Logic in Dortmund R Mesiar and Vil´em Nov´ ak 127 Contents IX Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets I Session Organiser: Krassimir T Atanassov Intuitionistic Fuzzy Graphs R Parvathi and M.G Karunambigai 139 On Some Intuitionistic Properties of Intuitionistic Fuzzy Implications and Negations Trifon A Trifonov and Krassimir T Atanassov 151 On Intuitionistic Fuzzy Negations Krassimir T Atanassov 159 Invited Session: Soft Computing Techniques for Reputation and Trust I Session Organiser: Ernesto Damiani A Simulation Model for Trust and Reputation System Evaluation in a P2P Network Roberto Aringhieri and Daniele Bonomi 169 A Fuzzy Trust Model Proposal to Ensure the Identity of a User in Time Antonia Azzini and Stefania Marrara 181 Quantification of the Effectiveness of the Markov Model for Trustworthiness Prediction Farookh Khadeer Hussain, Elizabeth Chang, and Tharam S Dillon 191 Applications II Fuzzy-Genetic Methodology for Web-based Computed-Aided Diagnosis in Medical Applications F de Toro, J Aroba, J.M Lopez 201 Weight Optimization for Loan Risk Estimation with Genetic Algorithm Irina Lovtsova 215 A Fuzzy Feature Extractor Neural Network and its Application in License Plate Recognition Modjtaba Rouhani 223 X Contents Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets II Session Organiser: Krassimir T Atanassov Nearest Interval Approximation of an Intuitionistic Fuzzy Number Adrian I Ban 229 On Intuitionistic Fuzzy Expert Systems With Temporal Parameters Panagiotis Chountas, Evdokiya Sotirova, Boyan Kolev and Krassimir Atanassov 241 Generalized Fuzzy Cardinalities of IF Sets Pavol Kr´ a´l 251 Invited Session: Soft Computing Techniques for Reputation and Trust II Session Organiser: Ernesto Damiani Towards Usage Policies for Fuzzy Inference Methodologies for Trust and QoS Assessment Stefan Schmidt, Robert Steele, Tharam Dillon 263 Simulating a Trust-Based Peer-to-Peer Metadata Publication Center Paolo Ceravolo, Alessio Curcio, Ernesto Damiani and Micol Pinelli 275 The Complex Facets of Reputation and Trust Karl Aberer, Zoran Despotovic, Wojciech Galuba and Wolfgang Kellerer 281 Theory I Fuzzy Covering Relation and Ordering: An Abstract Approach ˘ selja 295 Branimir Se˘ Measures of Differentiability ˘ Martin Kalina and Alexander Sostak 301 Lipschitz Continuity of Triangular Norms Andrea Mesiarov´ a 309 786 M Davoudi et al Fig Curve of the supposed farms with different conditions Fig Values of AT for the given farms Conclusion This paper conclusively reached to the point that any satisfactory operation of an automated irrigation system, particularly in a water-poor environment, is complex and requires realistic planning and rigorous implementation This has been achieved by developing proper fuzzy based decision making process We developed a software in MATLAB environment for the purpose of simulation Some typical farms have been considered and implemented in our toolbox The simulation results approved the aforementioned claim A Fuzzy-Based Automation Level Analysis 787 Fig 10 Comparison of the AT with automation levels References Shanan, L 1992 Planning and management of irrigation systems in developing countries Agricultural Water Management Journal 22 (1 and 2) October 1992 FAO 1979 Yield response to water FAO Irrigation and Drainage Paper 33 FAO, Rome FAO 1977 Crop water requirements FAO Irrigation and Drainage Paper 24 FAO, Rome IIMI 1989 Efficient Irrigation Management and System Turnover in Indonesia Final Report Vol Jantzen, J., 1998 Design of fuzzy controllers Technical Report No 98-E-864, 27pp “Fuzzy Logic and Soft Computing: Technology Development and Applications” By Piero P Bonissone IIMI 1987 Study of Irrigation Management in Indonesia Final Report Meijer, T.K.E 1992 Three pitfalls in irrigation design In: Irrigators and Engineers G Diemer and J Slabbers (eds.) Thesis Publishers, Amsterdam Plusquellec, H., Burt, C., and Wolters, H.W 1994 Modern water control in irrigation World Bank Technical Paper No 246 10 Piet Mondrian, Drought Monitoring and Assessment of Water Shortage Assessment of Water Shortage Mitigation Measures, WUEMED Workshop Roma, September 29-30, 2005 11 Pruitt, W.O., and J Doorenbos 1977 Empirical calibration a requisite for evapotranspiration formulae based on daily or longer mean climatic data Proceedings of the International Round Table Conference on “Evapotranspiration”, Budapest, Hungary 12 Tanner, C.B., and W.L Pelton 1960 Potential evapotranspiration estimates by the approximate energy balance method of Penman J Geophys Res., 65:3391–3413 13 Shanan, L 1992 Planning and management of irrigation systems in developing countries, Agricultural Water Management, Vol 23 (No1,2) 234 pp 14 Penman, H.L 1948 Natural evaporation from open water, bare soil and grass Proc Roy Soc London, A193:120–146 Motorized Skateboard Stabilization Using Fuzzy Controller Mohsen Davoudi, Mohammad Bagher Menhaj, and Mehdi Davoudi Summary In this paper, a fuzzy tuner is designed for a motorized skateboard to tune KI and KP coefficients of the PI controller to stabilize the skateboard rider The fuzzy if-then rules are derived from physical reactions of body against external forces The PI controller tracks the set point chosen by riders through a handle and applies a proper force to the skateboard to keep dynamic equilibrium of the person stable during the travel In this paper, through different simulations, it has been shown that the proposed controller make the system track the set point as quickly as possible while having a remarkably bigger traveling distance without any sort of instability problem Key words: Skateboard, Fuzzy controller, Stability, Motor scooter Introduction Motor scooter is a two-wheeled motor vehicle similar to a motorcycle or two-wheeled children’s vehicle resembling a skateboard with a handlebar Motorized skateboard is a four-wheeled vehicle The percentage of people Skate boarding is percent [1] Staff from the US Consumer Product Safety Commission (CPSC) recently conducted a special study to track injuries associated with powered scooters, a recreational product growing in popularity From July 2003 through June 2004, an estimated 10,015 powered scooter-related emergency room-treated injuries were reported through CPSC’s National Electronic Injury Surveillance System (NEISS) [1] Usually control of a motorized skateboard is done by a handle However for many people it is very difficult to learn how to control the skateboard and keep balance [2] The concept which is outlined in this paper is controlling a Motorized skateboard with a person standing on to keep dynamic equilibrium easily and increasing velocity and traveled distance as well Suppose that the skateboard has an electric motor, battery pack, a handle containing Start/Stop button which is run on a standard road The control of Skateboard consist of two main parts (1) determination of the physical parameters of the person standing on the skateboard, (2) changing the parameters of the PI controller 790 M Davoudi et al using data gathered in Section Physical parameters of a person needed in this analysis are weight, flexibility of the joints, length of every organ of the body such as shank, leg, chest, head, etc [3] To reach these objectives, we design a system to estimate amount of changes in the PI controller using the data driven from sensors on the skateboard and a fuzzy tuner In the first step, a pulse wave is sent to the driver of the electric motor to run the motor in a limited time, e.g., s This action leads to a pulse like force to both the skateboard and the person making a movement The person reacts to this force and starts to oscillate Every one has its own specific reaction [4] It depends on age, flexibility of joints, length, weight, etc [5] Then, the system records the velocity signal during the time Practically the velocity signal can be obtained through a shaft encoder sensor installed in a wheel of the skateboard Finally, by processing the velocity signal, getting information needed for the fuzzy tuner and applying some rules in the Fuzzy Inference System (FIS), the fuzzy tuner changes the coefficients KP and KI in the PI controller The rules are derived from practical skateboarding situations, physics of motion and perception of the people reaction on the skateboard, see figure Skateboard Model The modeled skateboard has 50 cm length and kg weight A general model is used for skateboard based on Euler Springs Euler Springs model stores no static energy in the skateboard and dynamic energy causes vibration in motion as shown in figure The resonant frequency is: ω= k = m g L Fig The block diagram of the skateboard fuzzy controller (1) Motorized Skateboard Stabilization 791 Fig Dynamic energy and vibration in motion B Ground F Prismatic B F skate mass earth Custom Joint Set point Joint Actuator Joint Sensor Fig Skate model implemented in MATLAB/Sim mechanics where k is the coefficient of the springs connected to the skate (related to the body model), m is the weight of the skateboard (m = kg), g is the Gravity and L is the displacement Because of puddles on the road the dynamic energy is changed during the time The resonant frequency usually is above 10 Hz (ω ≥ 10 Hz) This frequency is seen in the velocity signal but does not give us any useful information about the body parameters Signal processor contains a filter to eliminate the above frequencies Figure shows the skate model implemented in MATLAB/Sim mechanics This model contains an actuator (motor driver) and a sensor (shaft encoder) Body Model The postural balance system is one of the most fundamental functions for human voluntary motion This system has been analyzed and modeled by many researchers in the past In the field of biomechanics, many researchers work on the human balance control Some of them investigated features of balance control of the real human [3, 6] They actually applied perturbations to the real human, and measured the force, the velocity, or several physical parameters Others investigated the motion of balance recovery by stepping [6, 7] In these researches, the motion of the real human is analyzed For postural balance, Horak et al [8] found that 792 M Davoudi et al human use three strategies for keeping the balance, the ankle, hip, and arm strategy The ankle strategy is a strategy to use mainly the ankle joint to restore the position of the center of gravity back to the equilibrium state This strategy is chosen when the foot surface is long enough relative to the foot length, so that the subject can fully use his/her toes to push back the body When the foot surface is short relative to the foot length, the subject uses the hip and trunk joint to keep the balance, which is called the hip strategy Kuo et al [9] theoretically analyzed such strategies using the musculoskeletal model When a person is about to lose his/her balance, and is under a condition that he/she cannot step out one leg, the arms are recursively rotated to work as the final servo to move the center of mass back over the feet This strategy is effective in returning the center of mass over the feet, because the angular momentum of the trunk of the body is canceled out by the angular momentum generated by the rotation of the arms [10] In this paper, we propose a new human body model which is composed of five body segments, the shank, thigh, trunk, head, and arm The human-like body motion is obtained by rigid body, spring, and damper which are used for every joint in our body model The arm strategy appears without any prior feed-forward input when large perturbation force is applied to the body The motion of recovery closely resembles those by real human Figure shows the model of a sample joint in MATLAB/Sim mechanics Other joints are similar to this joint In order to model the above three strategies, feedback force is applied to the ankle, hip, and arm joints Signal processor The velocity signal is only reachable signal that is derived from shaft encoder sensor practically Velocity signal is given to two Band Pass Filters (BPF) Therefore, the vibration frequency is divided into two frequency bands (a) 2–4 Hz, (b) 4–10 Hz The first band indicates the low frequency vibration of the body and the second indicates high frequency vibration Relationships between signal parameters in these two bands help us find out the person’s body parameters and lead to model them into IF-THEN fuzzy rules For example, we found knee Spring & Damper3 Shank thigh B F Revolute Fig model of a sample joint in MATLAB/Sim mechanics Motorized Skateboard Stabilization 793 out that the signal of a short strong person on the skate in band (b) has a higher value of energy in comparison with band (a) because of rapid reactions to changes In this case, the skate can be run with a higher speed by means of increasing the KI coefficient For a tall weak inflexible person that has slow reactions, energy of band (a) is higher than energy of band (b) So a minimum value of KI and KP is needed to control the motion equilibrium We first calculate the energy of the velocity signal in these bands: S1 = ( f12 dt)1/2 (2) S2 = ( f22 dt)1/2 (3) where f1 is the output of the 2–4 Hz BPF and S1 is energy of the signal in this band f2 is output of the 4–10 Hz BPF and S2 is energy of the signal in this band Then, we count zero crossings in f1 and f2 signals within the experiment time to obtain the zc1 value and the zc2 value, respectively The following parameters zc1 A= (4) zc2 S1 B= (5) S2 S1 S2 C= + (6) zc1 zc2 are used to tune KP and KI by fuzzy tuner based on the aforementioned rules [10] The next step is to design the fuzzy tuner which is strongly dependent upon the physical reactions of the rider This information helps us determine fuzzy sets boundaries on every joint’s position and organs’ weights The Fuzzy tuner which is outlined in the next section has fixed rules Fuzzy Tuner In this section, a fuzzy tuner is introduced to tune the PI controller This tuner is described by the Following set of IF-THEN rules [11, 12] R1 : IF (A is SMALL) and (B is SMALL) and (C is HIGH), then (KI is H), (KP is H) R2 : IF (A is SMALL) and (B is SMALL) and (C is SMALL), then (KI is M), (KP is M) R3 : IF (A is SMALL) and (B is HIGH) and (C is HIGH), then (KI is H), (KP is LL) R4 : IF (A is HIGH) and (B is HIGH) and (C is SMALL), then (KI is L), (KP is LL) R5 : IF (A is HIGH) and (B is SMALL) and (C is MEDIUM), then (KI is HH), (KP is H) 794 M Davoudi et al In the above SMALL, MEDIUM, HIGH, and LL, L, M, H, HH are linguistic terms of antecedent fuzzy sets, and in the then parts LL, L, M, H, HH stand for very low, low, medium, high, very high, respectively We use a general form to describe these fuzzy rules [13]: Ri : IF (A is x1) and (B is x2) and (C is x3), then (KI is y1), (KP is y2), i = 1, ,16 where x1, x2, x3 are triangle-shaped fuzzy numbers and y1, y2 are fuzzy singletons An arbitrary fuzzy set A is depicted in Figure Figure represents the term sets of the output linguistic variable KI Let X and Y be the input and output space, and A, B, C be arbitrary fuzzy sets in X Then, a fuzzy set, [A, B, C] ◦ Ri in Y , can be determined by each Ri We use the sup-min compositional rule of inference [13, 14]: miKI i = µAi (x1).µB i (x2).µC i (x3) Fig An arbitrary fuzzy set A Fig Term sets of output linguistic variable (7) Motorized Skateboard Stabilization miKP i = µAi (x1).µB i (x2).µC i (x3) 795 (8) By using the center of area defuzzifier, we can obtain a crisp outputs KI, KP : KI = miKI i · y¯i , i = 1, , 16 miKI i (9) where y¯i is center of the KI i area KP = miKP i · y¯i , i = 1, , 16 miKP i (10) where y¯i is center of the KP i area Figure shows a 3-D surface plot of the above rules Simulation Results In this section we study four types of person’s body shown in Table [15, 16] The objective here is the PI controller makes the skateboard track the desired trajectory indicated by set point signal in block diagram shown in figure Fig surfaces of the fuzzy rules Table Four type of person’s body Number Tallness Weight Strength 180 160 190 140 cm cm cm cm 74 74 66 74 kg kg kg kg Average Strong Weak Strong 796 M Davoudi et al Fig A person on the skateboard in three states Fig The Set point signal As the set point signal increases, the risk of instability increases though it is dependent on the rider physical parameters [17, 18] Figure 8a shows a person in equilibrium state, (b) is a person in the threshold equilibrium state, and (c) is a person in tumble state In this experiment a positive pulse applied in t = s with a sec duration (altitude = 1) and a negative pulse applied in t = 20 s with s duration (altitude = 1) (Figure 9) The experiment contains a case in which an over exciting force leading to an immediate instability condition is applied Figure 10 shows the open loop simulation results for the first person whose parameters are given in Table Here we assumed that the traveled distance in 30 s is m Figure 11 shows a closed loop simulation results for KP = 1, KI = 0.2 In this case the traveled distance becomes 25 m Figure 12 shows the simulation results (velocity signal) when the parameters Kp and Ki which are adjusted by the proposed fuzzy tuner In this Motorized Skateboard Stabilization 797 Fig 10 Velocity signal (without using any feedback controller) Fig 11 Velocity signal (PI controller with constant parameters) case the traveled distance becomes 51 meters in 30 s Figure 13a shows the trajectory of controller parameter KI and figure 13b shows the trajectory of controller parameter KP Conclusion In this paper, a Fuzzy tuner has been developed for a motorized skateboard The tuner adjusts the parameters of a PI controller online The rule base designed for the tuner came from reactions of riders against external forces so that to keep dynamic equilibrium state As the set point signal increases, the risk of instability increases Thus, the PI controller tracks the desired set 798 M Davoudi et al Fig 12 Velocity signal (with fuzzy tuner) Fig 13 KI (a) and KP (b) coefficients point signal by applying proper forces to the skateboard to keep dynamic equilibrium state of the rider during traveling References David Blair, Li Ju and John Winterflood, The Investigation of Geometric Anti-springs Applied to Euler Spring Vibration Isolators, Eu-Jeen Chin Honours 2002 Wayne Wooten Simulations of Leaping, Tumbling, Landing, and Balancing Humans PhD thesis, Georgia Institue of Technology, 1997 Taku Komura, Yoshihisa Shinagawa, and Tosiyasu L Kunii Creating and retargetting motion by the musculoskeletal human body model The Visual Computer, (5):254–270, 2000 Motorized Skateboard Stabilization 799 Yi-Chung Pai and James Patton Center of mass velocity-position predictions for balance control Journal of Biomechanics, 30(4), 347–354, 1997 Hyeongseok Ko and Norman I Badler Animating human locomotion with inverse dynamics IEEE Computer M Oshita and A makinouchi A Dynamic Motion Control Technique for Human-like Articulated Figures Eurotraphics 2001, vol 20, Number 3, 2001 R Hayashi and S Tsujio High Performance Jumping Movements by Pendulumtype Jumping Machines In Proceedings of IEEE/RSJ International conferance on Intelligent Robots and Systems 722–727, Maui, USA, 2001 C Paul, R Dravid and F Iida Design and Control of a Pendulum Driven Hopping Robot to appear In Proc The IEEE/RSJ International Conference on Intelligent Robots and Systems Lausanne, Switzerland, 2002 Arthur D Kuo and Felix E Zajac Human standing posture: Multi-joint movement strategies based on biomechanical constraints Progress in Brain Research, 97, 2000 10 J Yen and R Langari, Fuzzy Logic: Intelligence, Control and Information, first ed., Prentice Hall, Berlin, 1998 Graphics and Applications, March:50–59, 1996 11 D Filev, R.R Yager, Essentials of Fuzzy Modeling and Control, WileyInterscience, New York, 1994 12 A Kaufmann, M.M Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications, Von Nostrand, New York, 1985 13 V Novak, I Perfilieva, J Mockor, Mathematical Principles of Fuzzy Logic, Kluwer, Boston/Dordrecht, 1999 14 T.J Ross, Fuzzy Logic with Engineering Applications, Second ed., Wiley, London, 2004 15 M.H Woollacott, von Hosten C., and Rosblad B Fixed patterns of rapid postural responses among leg muscles during stance Experimental Brain Research, 30:13–24, 1977 16 Wayne Wooten Simulations of Leaping, Tumbling, Landing, and Balancing Humans PhD thesis, Georgia Institue of Technology, 1997 17 Yi-Chung Pai and James Patton Center of mass velocity-position predictions for balance control Journal of Biomechanics, 30(4), 347–354, 1997 18 Elisabetta Papa, Aurelio Cappozzo A telescopic inverted-pendulum model of the musculo-skeletarl system and its use for the analysis of the sit-to-stand motor task Jounal of Biomechanics, 32:1205–1212, 1999 Index Aberer, Karl 281 Aizenberg , Igor 441, 457 Aringhieri, Roberto 169 Aroba, J 201 Astola, Jaakko 441 Atanassov, Krassimir 151, 159, 241 Azzini, Antonia 181 Ban, Adrian 229 Baniamerian, Amir 757 Battersby, Alan 597 Berlik, Stefan 603 Bodyanskiy, Yevgeniy 41, 647 Bonomi, Daniele 169 Borgulya, Istv´ an 577 Borisov, Arkady 555 Carlsson, Christer Ceravolo, Paolo 275 Chang, Elizabeth 191, 425 Chen, Shouyu 721 Chountas, Panagiotis 241 Curcio, Alessio 275 Damiani, Ernesto 275, 425 Davoudi, Mehdi 777, 789 Davoudi, Mohsen 777, 789 De Toro Negro, Francisco 201 De Witte, Val´erie 711 Dempe, Stephan 405 Despotovic, Zoran 281 Dillon, Tharam 191, 263, 425 El-Mihoub, Tarek 597 Entani, Tomoe 415 Etemad-Shahidi, Amir Farshad 741 Full´er, Robert Galuba, Wojciech 281 Ganyun, LV 117 Garc´ıa Serrano, Ana 325 Garc´ıa-Lapresta, Jos´e Luis 561 Garimella, Ramamurthy 473 Gerdelan, Antony 699 Ghasempour Shirazi, Seyed Alireza 85 Giurca, Adrian 513 Gorjanac-Ranitovic, Marijana 375 Gorshkov, Yevgen 647 Gottwald, Siegfried 653 Gries, Thomas 93 Guadarrama, Sergio 335, 353 Guo, Yu 721 Hă ullermeier, Eyke 615 Hata, Yutaka 733 Higo, Tomoaki 437 Hirose, Akira 437 Hopggod, Adrian 597 Hussain, Farookh 191 Iancu, Ion 513 Iseri, Kensuke 733 Johany´ ak, Zsolt Csaba 499 802 Index Kacprzyk, Janusz 19 Kalina, Martin 301 Karrari, Mehdi 765 Karunambigai, M.G 139 Kasabov, Nikola 521 Kellerer, Wolfgang 281 Kerre, Etienne E 711 Kobashi, Syoji 733 Kokshenev, Illya 647 Kolev, Boyan 241 Kolodyazhniy, Vitaliy 41, 647 Kondo, Katsuya 733 Kov´ acs, Szilveszter 485, 499 Kr al, Pavol 251 Krewet, Carsten 105 Kuhlenkă otter, Bernd 105 Kyosev, Yordan 93 Llamazares, Bonifacio 561 Lopez, J.M 201 Lovtsova, Irina 215 Madhavan, K.S 751 Majlender, P´eter Marques Pereira, Ricardo 15 Marrara, Stefania 181 Mart´ınez-Panero, Miguel 561 Martyna, Jezy 637 Maturo, A 29 Menhaj, Mohammad Bagher 51, 61, 75, 85, 757, 765, 777, 789 Mesiar, Radko 127, 629 Mesiarov´ a, Andrea 309 Misina, Sigita 545 Moˇckoˇr, Jiˇr´ı 677 Moraga, Claudio 367, 441 Mousavi, Seyed Jamshid 741 Murthy, T.S.R 751 Nachtegael, Mike 711 Navara, Mirko 667 Nolle, Lars 587, 597 Nov´ ak, Vil´em 127, 683 Poyedyntseva, Valeriya 41 Rajati, Mohammad Reza 75 Rastin, Sepideh J 61 Razi, Kamran 51 Reinbach, Ingo 93 Renedo, Eloy 335, 353 Reusch, Bernd 603 Reyes, Napoleon 699 Ribeiro, Rita Almeida 15 Rouhani, Modjtaba 223 Schmidt, Stefan 263 Schulte, Stefan 711 ˇ Seda, Miloˇs 395 Serra, Paulo J.A 15 ˇ selja, Branimir 295 Seˇ Sethuraman, R 751 Shahingohar, Aria 765 ˇ Sostak, Alexander 301 Sotirova, Evdokiya 241 Squillante, Massimo 29 ˇ Stajner-Papuga, Ivana 383 Starostina, Tatiana 405 Steele, Roberto 263 Stephan, Andreas 41 Sukov, Anatoly 555 Talavatifard, Habib Allah 51 Tanaka, Hideo 415 Taniguchi, Kazuhiko 733 Tepavˇcevi´c, Andreja 375 Trifonov, Trifon 151 Trillas, Enric 335, 353 Van der Weken, Dietrich 711 Ventre, Aldo 29 Wiguna, Wiratna S 325 Wilbik, Anna 19 Wu, Li 721 Xiaodong, Wang 117 Orlowska, Ewa 323 Yaghamae, Mohammad Hossein 765 Paliy, Dmitriy 441 Parvathi, R 139 Peeva, Ketty 93 Perfilieva, Irina 691 Petr´ık, Milan 667 Pinelli, Micol 275 Zadeh, Lotfi A Zadro˙zny, Slawomir 19 Zanganeh, Morteza 741 Zhang, Xiang 105 Zhou, Huicheng 721 ... through the use of methods based on bivalent logic and probability theory; and (b) to introduce and outline a collection of nonstandard concepts, ideas, and tools which are needed to achieve a quantum... Information Processing and Web Mining, 2005 ISBN 3-540-25056-5 Bernd Reusch, (Ed.) Computational Intelligence, Theory and Applications, 2005 ISBN 3-540-22807-1 Abraham Ajith, Bernard de Bacts, Mario... Applications and Soft Computing, 2006 ISBN 3-540-29123-7 Bernd Reusch (Ed.) Computational Intelligence, Theory and Applications International Conference 9th Fuzzy Days in Dortmund, Germany, Sept