Advances in Soft Computing - AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems. Calcutta, India, February 3-6, 2002. ProceedingsNikhil R. Pal, Michio Sugeno It is our great pleasure to welcome you all to the 2002 AFSS International Conference on Fuzzy Systems (AFSS 2002) to be held in Calcutta, the great City of Joy. AFSS 2002 is the ?fth conference in the series initiated by the Asian Fuzzy Systems Society (AFSS). AFSS 2002 is jointly being organized by theIndianStatisticalInstitute(ISI)andJadavpurUniversity(JU). Likeprevious conferencesinthisseries,wearesure,AFSS2002willprovideaforumforfruitful interaction and exchange of ideas between the participants from all over the globe. The present conference covers all major facets of soft computing such as fuzzy logic, neural networks, genetic algorithms including both theories and applications. Wehopethismeetingwillbeenjoyableacademicallyandotherwise. We are thankful to the members of the International Program Committee and the Area Chairs for extending their support in various forms to make a strong technical program. Each submitted paper was reviewed by at least three referees, and in some cases the revised versions were again checked by the ref- ees. As a result of this tough screening process we could select only about 50% of the submitted papers. We again express our sincere thanks to all referees for doing a great job. We are happy to note that 19 di?erent countries from all over the globe are represented by the authors, thereby making it a truly inter- tional conference. We are proud to have a list of distinguished speakers including Profs. Z. Pawlak, J. Bezdek, D. Dubois, and T. Yamakawa. |
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Contents
A New Perspective on Reasoning | 1 |
On Interpretability of Fuzzy Models | 12 |
Average measure dravr | 17 |
Wxws | 18 |
Degree of Similarity in Fuzzy Partition | 20 |
T | 25 |
Fuzzy Set in Default Reasoning | 27 |
r | 32 |
Towards Fuzzy Calibration | |
FuzzySimilarityBased Image Noise | |
GeneticFuzzy Approach | |
XX X | |
10 | |
Evolutionary Approaches to Rule Extraction | |
EN ŠTE | |
Parallelized Crowding Scheme | |
Interpolation in Hierarchical RuleBases | 34 |
The DempsterShafer Approach to MapBuilding | 40 |
w m | 42 |
20 | 45 |
The Fuzzy Model for Aircraft Landing Control | 47 |
w | 53 |
Implementation of Nonlinear Fuzzy Models | 55 |
A Gain Adaptive Fuzzy Logic Controller | 62 |
A Traffic Light Controlling FLC | 69 |
X2 | 72 |
B | 73 |
Vehicle Routing Scheduling and Dispatching System | 76 |
Similarity between Fuzzy Multiobjective Control | 85 |
A Fuzzy Goal Programming Approach | 91 |
Material Handling Equipment Selection | 99 |
The Rough Set View on Bayes Theorem | 106 |
Table 4 Decision table | 114 |
Verbalizing Computers | 117 |
Soft Computing Based EmotionIntention Reading | 121 |
Generalization of Rough Membership Function | 129 |
A Class of QuantitativeQualitative Measures | 136 |
generalise Pawlaks method 24 | 142 |
Wavelet Transform Based Fuzzy Inference System | 148 |
三國 | 152 |
W VV | 153 |
nu | 154 |
A FuzzyNeural Technique for Flashover Diagnosis | 156 |
Feature Identification for Fuzzy Logic | 163 |
SoftComputing Technologies | 171 |
Comparative Study between Different | 178 |
Static Signature Verification | 185 |
7 | 202 |
Modeling of Nonlinear Systems | 204 |
For | 206 |
s | 207 |
M | 211 |
hhhh | 212 |
Characterization of Nonlinear Cellular | 214 |
statistically independent patterns is 0 The ordered CA rules do | 220 |
On Convergence | 221 |
一 | 226 |
Recognition of Handprinted Bangla Numerals | 228 |
Optimal Synthesis Method for Binary Neural Network | 236 |
declared true vertices 000 010 011 | 240 |
1 | 241 |
By ETL requires five neurons in hidden layer as in | 242 |
3 | 243 |
x | 244 |
A Neural Network Based Seafloor Classification | 245 |
Designing RuleBased Classifiers with OnLine | 251 |
M MLM | 257 |
Decomposed Neurofuzzy ARX Model | 260 |
Weather Forecasting System Based on Satellite | 267 |
V | 271 |
Evolutionary Subsethood Product | 274 |
VSS Learning Based Intelligent Control | |
M | |
Some Notes on Alternating Optimization | |
Noisy Speech SegmentationEnhancement | |
Towards Optimal Feature and Classifier | |
Fuzzy CMeans ClusteringBased | |
Noise ClusteringBased Speaker Verification | |
A Combination Scheme for Fuzzy Clustering | |
Clustering of Symbolic Data and Its Validation | |
Generalised Fuzzy Hidden Markov Models | |
On Generalization and KFold Cross Validation | |
Pictorial Indexes and Soft Image Distances | 6 |
Fig 2 The fuzzysets generated by distance | 11 |
Stereo Correspondence Using a Fuzzy Approach | |
a | 1 |
Implementation of BTTC Image Compression Algorithm | 5 |
Fig 6 Original lisaw image domain triangulation for | 10 |
Applications of the ILF Paradigm | 12 |
FA | |
a | |
On OCR of Degraded Documents | |
A Bootstrapped Modular Learning Approach | |
+ | |
d1 | |
n | |
A GAFUZZY Approach | |
Soft Computing in ECommerce | |
Bridging Agent Technology | |
Fuzzy Points and Fuzzy Prototypes | 6 |
Some Injective Cogenerators in Fuzzy Topology | 11 |
Derivative and Differential of Convex Fuzzy | 18 |
A Topology for Fuzzy Automata | 25 |
Composition of | iii |
The Lower and Upper Approximations | viii |
TFuzzy Hyperalgebraic Systems | |
On Some Weaker Forms of Fuzzy Compactness | |
Some Results on Fuzzy Commutative | |
Preface | |
Organization | |
Table of Contents | |
Hybrid and Embedded Software Technologies for | 1 |
Numerical Methods for Differential Systems | 3 |
The Missing Link in | 5 |
Hybrid System Models of Navigation Strategies | 7 |
obstacles | 11 |
Fig 3 An actor lightest object | 15 |
Hybrid Control of a Truck and Trailer Vehicle | 21 |
N | 32 |
Reachability Analysis of Hybrid Systems via | 35 |
Towards Computing Phase Portraits of | 49 |
CHE | 55 |
Dynamical Qualitative Analysis of Evolutionary | 62 |
Design of Observers for Hybrid Systems | 76 |
Guaranteed Overapproximations of Unsafe Sets | 90 |
1 | 104 |
On the Optimal Control Law for | 105 |
A Computational Framework for | 120 |
a | 122 |
A Comparison of Control Problems | 134 |
Hybrid Control Loops AD Maps | 149 |
Switching and Feedback Laws for Control of | 164 |
Quantized Stabilization of TwoInput Linear | 179 |
telecommunications Wiley New York 1991 | 193 |
Analysis of DiscreteTime PWA Systems with | 194 |
Modeling and Control of Cogeneration Power | 209 |
Exploiting Implicit Representations in Timed | 225 |
While our model was developed for CIRCA it is | 238 |
Computation of RootMeanSquare Gains | 239 |
Mode Estimation of Probabilistic Hybrid | 253 |
Symmetry Reduction of a Class of | 267 |
M | 271 |
Bisimulation Based Hierarchical System | 281 |
t | 286 |
+ | 288 |
Qualitative Modeling and Heterogeneous Control | 294 |
a | 300 |
70 | 305 |
An Approach to ModelBased Diagnosis of | 308 |
三 | 314 |
InformationBased AlphaBeta Search and the | 323 |
Synthesis of Robust Control Systems under | 337 |
Optimal Control of Quantized Input Systems | 351 |
AHMAN | 360 |
Fig 6 The trajectories of the base variables | 362 |
Reconfiguration in Hierarchical Control | 364 |
Hybrid Kernels and Capture Basins for Impulse | 378 |
fou | 382 |
Ordered Upwind Methods for Hybrid Control | 393 |
En | 400 |
0 | 401 |
86 | 404 |
8888 | 405 |
DiscreteTime Refinement of Hybrid Automata | 407 |
I | 421 |
Zo | 429 |
Composing Abstractions of Hybrid Systems | 436 |
Optimal Control of Hysteresis in Smart | 451 |
Series of Abstractions for Hybrid Automata | 465 |
Author Index | |
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Common terms and phrases
abstract action algorithm analysis application approach approximation associated assume automata automaton called closed clustering complex Computer considered constraints continuous convergence corresponding decision defined Definition denote described determined discrete dynamics elements equation estimation example exists fault Figure finite function fuzzy sets given hybrid systems IEEE implementation initial input introduced layer learning linear logic means measure membership functions method mode neural node Note objective observer obtained operator optimal output parameters partition pattern performance plant positive possible presented problem properties proposed provides recognition References region relation represents respectively rules sampling satisfied selection shown signal similarity solution solve space stability step structure subset switching Table techniques Theorem theory trajectory transition University variables vector weight