NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society最新文献

{"title":"Design of a Novel Fuzzy Controller to Enhance Stability of Vehicles","authors":"M. Biglarbegian, W. Melek, F. Golnaraghi","doi":"10.1109/NAFIPS.2007.383874","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383874","url":null,"abstract":"This paper presents the design of a novel fuzzy control structure to improve stability of vehicles with semi-active suspension system. The proposed fuzzy controller adjusts the damping coefficient to stabilize the sprung mass and hence reduce the tendency of vehicle to rollover. A full car model with eight degrees of freedom is adopted that includes the vertical, roll, yaw, and pitch motions as well as the vertical motions of each wheel. Four decentralized fuzzy controllers are developed and applied to each individual damper in the vehicle suspension system. The controllers input(s) are lateral acceleration and vehicle states and the output is an adaptive damping coefficient. Mamdani's inference engine is used to obtain the required damping coefficient of each suspension system. To evaluate the performance of the proposed controller, experiments were performed for simple turn and lane change maneuvers. To show the effectiveness of the proposed controller, comparison is made with Cadillac controller. Results show that the fuzzy controller reduces roll angle, linear transfer ration (LTR) and hence decreases the propensity to rollover in vehicles.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"28 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114926508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Fuzzy-Immune-PID Controllers Design of PWM DC-DC Converters","authors":"C. Hsieh","doi":"10.1109/NAFIPS.2007.383823","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383823","url":null,"abstract":"Generally, the state-space averaged model, which is an approximate model, is employed to synthesize the PWM (pulse-width-modulated) DC-DC converters. Instead, an algorithm based on orthogonal-functions approach (OFA) only involving algebraic computation is proposed in this paper to precisely solve the discontinuous dynamic equations of the PWM DC-DC converters. On the other hand, to accommodate the load variation of a PWM DC-DC converter, a fuzzy-immune-PID controller by fusing the conventional PID controller, the fuzzy logic theory and the immune feedback law is considered as a self-adaptive controller in this paper. Based on the OFA, the optimal fuzzy-immune-PID controller design problem for a class of PWM DC-DC converters is transformed into a static-parameters optimization problem represented by algebraic equations. Then for the static optimization problem, the hybrid Taguchi-genetic algorithm (HTGA) is employed to find the optimal parameters of the fuzzy-immune-PID controllers for the PWM DC-DC converters under the criterion of minimizing an integral quadratic performance index. The proposed integrative method, which fuses the OFA and the HTGA, is non-differential, non-integral, straightforward, and well-adapted to computer implementation. The results show that the proposed method gives an effective way for synthesizing the optimal parameters of the fuzzy-immune-PID controllers of the PWM DC-DC converters.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116680198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Sets and Medicine Historical and Epistemological Remarks","authors":"R. Seising","doi":"10.1109/NAFIPS.2007.383914","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383914","url":null,"abstract":"The field of medicine is notably different from other sciences since the great complexity of living organisms makes it nearly impossible to make definite statements and observations, i.e. to have certain knowledge. Fuzzy Sets and Systems have been proposed as a appropriate tool to deal with this unsharp medical knowledge since the late 1960's. In the first part of tis paper we give a historical sketch of the development of this \"fuzzy thinking\" in the field of medicine. In the second part of this paper we regard this \"fuzzy thinking\" in medicine from a philosophical point of view. We consider the fuzziness of medical knowledge as the fuzziness of scientific knowledge at all.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124407539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computing Statistical Characteristics When We Know Probabilities with Interval or Fuzzy Uncertainty: Computational Complexity","authors":"G. Xiang, J. W. Hall","doi":"10.1109/NAFIPS.2007.383904","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383904","url":null,"abstract":"In traditional statistics, we usually assume that we know the exact probability distributions. In practice, we often only know the probabilities with interval uncertainty. The main emphasis on taking this uncertainty into account has been on situations in which we know a cumulative distribution function (cdf) with interval uncertainty. However, in some cases, we know the probability density function (pdf) with interval uncertainty. We show that in this situations, the exact range of some statistical characteristics can be efficiently computed. Surprisingly, for some other characteristics, similar statistical problems which are efficiently solvable for interval-valued cdf become computationally difficult (NP-hard) for interval-valued pdf.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123138491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast Recursive Method to Compute the Generalized Centroid of an Interval Type-2 Fuzzy Set","authors":"M. Melgarejo","doi":"10.1109/NAFIPS.2007.383835","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383835","url":null,"abstract":"This article presents a recursive algorithm to compute the generalized centroid of an interval type-2 fuzzy set. First, a re-expression of the upper and lower limits of the generalized centroid is introduced. Then, the re-expressed formulas are solved by using a mixed approach of exhaustive search and recursive computations. This method is compared with the Karnik-Mendel iterative algorithm under the same computational principles. Experimental evidence shows that the recursive approach is computationally faster than the Karnik-Mendel method without loosing numeric precision.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130226266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additive Weighted Ordered Weighted Average","authors":"T. Whalen","doi":"10.1109/NAFIPS.2007.383871","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383871","url":null,"abstract":"This paper presents a simplified version of Tora's Weighted Ordered Weighted Average based on restricting the Ordered Weighted Average component to a Soft Hurwicz Rule. It provides an easily understood, easily implemented tool that respects both the relative inherent importance of criteria and the degree to which tradeoffs among them are appropriate.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131571354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fitting a Normal Distribution to Interval and Fuzzy Data","authors":"G. Xiang, V. Kreinovich, S. Ferson","doi":"10.1109/NAFIPS.2007.383901","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383901","url":null,"abstract":"In traditional statistical analysis, if we know that the distribution is normal, then the most popular way to estimate its mean a and standard deviation sigma from the data sample x1,..., xn is to equate a and sigma to the arithmetic mean and sample standard deviation of this sample. After this equation, we get the cumulative distribution function F(x) = phi (x-a/sigma) of the desired distribution. In many practical situations, we only know intervals [xi, xi] that contain the actual (unknown) values of xi or, more generally, a fuzzy number that describes xt. Different values of xt lead, in general, to different values of F(x). In this paper, we show how to compute, for every x, the resulting interval [F_(x),F(x)] of possible values of F(x) -or the corresponding fuzzy numbers.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127773045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive T-S type Rough-Fuzzy Inference System (ARFIS) for Pattern Classification","authors":"ChangSu Lee, A. Zaknick, T. Braunl","doi":"10.1109/NAFIPS.2007.383822","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383822","url":null,"abstract":"The Rough-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a new Adaptive T-S type rough-fuzzy inference system (ARFIS) for pattern classification. Rough set theory is utilized to reduce the number of attributes and also to obtain a minimal set of decision rules based on input-output data sets. A T-S type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering algorithm and rough set theory, respectively. The generated T-S type rough-fuzzy inference system is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a validity checking for the minimal set of rules. The proposed ARFIS is able to reduce the number of rules which increases exponentially when more input variables are involved and also to assess the validity of the minimized decision rules. The performance of the proposed ARFIS is compared with other existing pattern classification schemes using Fisher's Iris and Wisconsin breast cancer data sets and shown to be very competitive.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121155602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fuzzy Message Priority Arbitration Approach for Sensor Networks","authors":"J. Rivera, G. Bojorquez, M. Chacon, G. Herrera, M. Carrillo","doi":"10.1109/NAFIPS.2007.383906","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383906","url":null,"abstract":"This paper presents a novel fuzzy multinode communication priority protocol for intelligent network sensors. Each node is part of an intelligent sensor network. The node nN basically consists of three logic blocks: a digital interface, application, and network communication. The network communication is across a serial bus system defined as controller area network (CAN). The fuzzy arbitration system was designed with simulation software assuming two variables. The first variable corresponds to the message size or size of the information to be transmitted and the second variable is the rate of change of the sensor variable. A dynamic auto-programming priority system was developed with a range of 0 to 15 priorities. The system is implemented with nine fuzzy rules. The best defuzzification approach was obtained from an experiment using five different methods. In order to evaluate the performance of the proposed method it is compared against a static approach. The FMPAS approach can work like a dynamic priority arbitration system and it is a new alternative to avoid the static disadvantages of other paradigms. Besides, the proposed methodology establishes the fundamentals to create reconfigurable communication in intelligent network sensors.","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116773823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Clustering of Baseball Statistics","authors":"B. Bushong","doi":"10.1109/NAFIPS.2007.383812","DOIUrl":"https://doi.org/10.1109/NAFIPS.2007.383812","url":null,"abstract":"A previous investigation into the ability of fuzzy clustering to be a sound method for comparing Major League Baseball players' batting averages yielded promising results. Yet, the study involved a rather small sample of 90 batting averages, which were fuzzy clustered into three categories. Furthermore, the primary study focused on batting averages, a statistic that is incapable of reflecting a player's hitting ability on its own, and it certainly does not account for the defensive skill of a player. While the original work highlighted some of the inherent advantages to fuzzy clustering, the small sample size and number of groups did not allow for a complete spectrum to be generated. Without an uncondensed continuum from which to compare the relative overall skills of all players, the original results limited the practical applications of fuzzy clustering in Major League Baseball. The current research aims to greatly improve upon the first study and emphasize the potential gains from the implementation of fuzzy clustering in the practices of Major League Baseball. In an effort to provide a more comprehensive analysis of baseball statistics, this investigation includes two additional hitting statistics, on base percentage and slugging percentage, and incorporates fielding percentage. The three added statistics reflect a player's bat control, power, and defensive reliability, respectively, all of which teams use to gauge a player's skills. All three offensive statistics are averaged to generate an inclusive measure of a player's offensive capabilities, and the corresponding fielding percentage was added as a second dimension into the fuzzy clustering program. The new four-input model is a more developed and more applicable version of the one produced in the original research. Fuzzy clustering of batting averages, on base percentages, slugging percentages, and fielding percentages is an innovative way for teams to compare an individual's skills to that of all professional players simultaneously, since fuzzy clustering is ideal for establishing relationships between data that would not normally be associated. Baseball statisticians will no longer be forced to merely note the numerical difference between players' three key hitting statistics and a critical defensive measure. Instead, players can be grouped according to their relative production, providing organizations with a more comprehensive view of players' capabilities. In this investigation, 968 Major League Baseball players' selected statistics were fuzzy clustered into nine groups, in an effort to better express the range of baseball skills. The results of the research offer insight into an amount of data that cannot efficiently be processed by an individual, which would make fuzzy clustering of batting averages an invaluable tool for Major League Baseball. Motivational resources are greatly needed in such a mentally and emotionally draining sport, and fuzzy-clustered statistics would provide ","PeriodicalId":292853,"journal":{"name":"NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116840504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}