Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583618
C. Shih, C.S. Wang
This paper introduces a design methodology using fuzzy theory to find random design variables by maximizing the reliability as well as optimizing multiobjectives. The formulation of the problem involves random parameters and probabilistic and fuzzy probabilistic constraints. The objective weighting strategy in the multiobjective fuzzy formulation is presented. An engineering design example illustrates this optimization process and the solution techniques.
{"title":"Multiobjective fuzzy and stochastic engineering optimization with maximizing reliability","authors":"C. Shih, C.S. Wang","doi":"10.1109/AFSS.1996.583618","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583618","url":null,"abstract":"This paper introduces a design methodology using fuzzy theory to find random design variables by maximizing the reliability as well as optimizing multiobjectives. The formulation of the problem involves random parameters and probabilistic and fuzzy probabilistic constraints. The objective weighting strategy in the multiobjective fuzzy formulation is presented. An engineering design example illustrates this optimization process and the solution techniques.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132815550","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583646
Jyh-Da Wei, Chuen-Tsai Sun
Hysteresis is an effect of memory, which is frequently observed in the realm of nature. The purpose of this paper is to try to understand more of it, such that we may achieve better performance from the systems which are hysteresis-embedded. A hypothesis-based neural network model is offered in this paper, the synchronous delay network (SDN) model. It can be realized as a feedforward neural network. We also discuss the possible applications in this paper.
{"title":"A neural network model of hysteresis","authors":"Jyh-Da Wei, Chuen-Tsai Sun","doi":"10.1109/AFSS.1996.583646","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583646","url":null,"abstract":"Hysteresis is an effect of memory, which is frequently observed in the realm of nature. The purpose of this paper is to try to understand more of it, such that we may achieve better performance from the systems which are hysteresis-embedded. A hypothesis-based neural network model is offered in this paper, the synchronous delay network (SDN) model. It can be realized as a feedforward neural network. We also discuss the possible applications in this paper.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172137","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583560
Chir-Ho Chang, Hsien-Hui Tseng, Bor-Yao Huang
The performance of a neural fuzzy intelligent recognition system (NFIRS) which recognizes varied levels of noise corrupted characters was investigated. The number of regions in the universe of discourse of the input space was first arbitrarily selected. Then, the centers of these regions were self organized by feeding the system with a 256-pixel alphabet and algebraic training samples to the Kohonen competitive learning network. Based on the reallocated centers, we tried several combinations of varied rule region product in order to generate a smaller set of fuzzy rules. We fixed the number of features for simulation, and to simplify and isolate the effect of rule extraction. Simulation results showed a NFIRS that uses a set of thirty six sampling data set as the training input will generate a set of thirty six if-then fuzzy rules which can be used to recognize a corrupted testing data set without sacrificing the rate of recognition under varied conditions.
{"title":"Noise immunization of a neural fuzzy intelligent recognition system by the use of feature and rule extraction technique","authors":"Chir-Ho Chang, Hsien-Hui Tseng, Bor-Yao Huang","doi":"10.1109/AFSS.1996.583560","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583560","url":null,"abstract":"The performance of a neural fuzzy intelligent recognition system (NFIRS) which recognizes varied levels of noise corrupted characters was investigated. The number of regions in the universe of discourse of the input space was first arbitrarily selected. Then, the centers of these regions were self organized by feeding the system with a 256-pixel alphabet and algebraic training samples to the Kohonen competitive learning network. Based on the reallocated centers, we tried several combinations of varied rule region product in order to generate a smaller set of fuzzy rules. We fixed the number of features for simulation, and to simplify and isolate the effect of rule extraction. Simulation results showed a NFIRS that uses a set of thirty six sampling data set as the training input will generate a set of thirty six if-then fuzzy rules which can be used to recognize a corrupted testing data set without sacrificing the rate of recognition under varied conditions.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128897195","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583597
Nan-Hui Lin, J.C. Chen
How can fuzzy-nets technology perform with greater accuracy and efficiency? The purpose of this paper is to identify the optimal factor-level combinations of a simulation model using the backing up of a truck. Taguchi Parameter Design with an L/sub 0/ (3/sup 4/) orthogonal array was employed to diminish the number of treatment runs.
{"title":"Evaluation of fuzzy-nets training efficiency","authors":"Nan-Hui Lin, J.C. Chen","doi":"10.1109/AFSS.1996.583597","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583597","url":null,"abstract":"How can fuzzy-nets technology perform with greater accuracy and efficiency? The purpose of this paper is to identify the optimal factor-level combinations of a simulation model using the backing up of a truck. Taguchi Parameter Design with an L/sub 0/ (3/sup 4/) orthogonal array was employed to diminish the number of treatment runs.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292831","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583559
Chih-Keng Chen, Yucong Wang
In this paper, we develop the fuzzy controller for an ABS (anti-lock braking system). The system models of the ABS and the fuzzy controller structure are discussed. Computer simulations are given to understand the effect of some important parameters.
{"title":"Fuzzy control for the anti-lock brake system","authors":"Chih-Keng Chen, Yucong Wang","doi":"10.1109/AFSS.1996.583559","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583559","url":null,"abstract":"In this paper, we develop the fuzzy controller for an ABS (anti-lock braking system). The system models of the ABS and the fuzzy controller structure are discussed. Computer simulations are given to understand the effect of some important parameters.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"946 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123307882","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583621
Jac-Kal Uk, K. Hoon
The objective of this paper is to demonstrate how fuzzy dynamic systems can show chaotic phenomena and chaotic dynamics similar to those found in a class of nonlinear systems. We found that the fuzzy chaotic dynamic model of a cubic map results in the same bifurcation diagrams, and that it shows stable equilibrium points, period-doubling and chaotic attractors.
{"title":"Chaotic behaviors in fuzzy dynamic systems: \"fuzzy cubic map\"","authors":"Jac-Kal Uk, K. Hoon","doi":"10.1109/AFSS.1996.583621","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583621","url":null,"abstract":"The objective of this paper is to demonstrate how fuzzy dynamic systems can show chaotic phenomena and chaotic dynamics similar to those found in a class of nonlinear systems. We found that the fuzzy chaotic dynamic model of a cubic map results in the same bifurcation diagrams, and that it shows stable equilibrium points, period-doubling and chaotic attractors.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123927397","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583598
Z.W. Zhang, P. Anderson, B. Bignall
As the application domain of neuro-fuzzy inferencing systems becomes broader, users will find increasing difficulty in selecting the right models for their specific application. This paper describes the design and implementation of a general neuro-fuzzy inference environment (GENIE). Its purpose is to facilitate the assessment of new learning strategies and control models. GENIE includes a number of well known neuro-fuzzy inference (NFI) systems. Besides its control, GENIE gives users a graphical display of membership functions and the system being controlled, thereby allowing users to visually monitor the changes occurring inside the controller and the system being controlled. Two new performance metrics for neuro-fuzzy controllers are described that have been incorporated into GENIE.
{"title":"GENIE: a general neurofuzzy inference environment","authors":"Z.W. Zhang, P. Anderson, B. Bignall","doi":"10.1109/AFSS.1996.583598","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583598","url":null,"abstract":"As the application domain of neuro-fuzzy inferencing systems becomes broader, users will find increasing difficulty in selecting the right models for their specific application. This paper describes the design and implementation of a general neuro-fuzzy inference environment (GENIE). Its purpose is to facilitate the assessment of new learning strategies and control models. GENIE includes a number of well known neuro-fuzzy inference (NFI) systems. Besides its control, GENIE gives users a graphical display of membership functions and the system being controlled, thereby allowing users to visually monitor the changes occurring inside the controller and the system being controlled. Two new performance metrics for neuro-fuzzy controllers are described that have been incorporated into GENIE.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801961","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583577
M. Umano, T. Imada, I. Hatono, H. Tamura
Ordinary object oriented databases have been eagerly studied. We already proposed a fuzzy object oriented database that can treat ambiguous attribute values with certainty factors and ambiguous inheritance using fuzzy sets (M. Umano et al., 1995). We implement an SQL type data manipulation language and demonstrate it using several examples.
普通的面向对象数据库已经得到了广泛的研究。我们已经提出了一种面向模糊对象的数据库，它可以用确定性因素处理模糊属性值，并使用模糊集处理模糊继承(M. Umano et al.， 1995)。我们实现了一种SQL类型的数据操作语言，并使用几个示例进行了演示。
{"title":"Implementation of SQL-type data manipulation language for fuzzy object-oriented databases","authors":"M. Umano, T. Imada, I. Hatono, H. Tamura","doi":"10.1109/AFSS.1996.583577","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583577","url":null,"abstract":"Ordinary object oriented databases have been eagerly studied. We already proposed a fuzzy object oriented database that can treat ambiguous attribute values with certainty factors and ambiguous inheritance using fuzzy sets (M. Umano et al., 1995). We implement an SQL type data manipulation language and demonstrate it using several examples.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125398016","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583595
N. Chang, H.W. Chen
This paper presents a modified formulation of a fuzzy multiobjective programming model in order to illustrate the tendency of nonlinearity in many environmental problems. The genetic algorithm is described as a tool to solve a typical water pollution control problem.
{"title":"The application of genetic algorithm and nonlinear fuzzy programming for water pollution control in a river basin","authors":"N. Chang, H.W. Chen","doi":"10.1109/AFSS.1996.583595","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583595","url":null,"abstract":"This paper presents a modified formulation of a fuzzy multiobjective programming model in order to illustrate the tendency of nonlinearity in many environmental problems. The genetic algorithm is described as a tool to solve a typical water pollution control problem.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130008069","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}
Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583561
Hung-Chang Lee, Tao Wang
Like a dawn light scattering into the cloud sky of AI, neural network and fuzzy logic become state-of-the-art technologies in exploring the intellect. To make a judgement between both technologies, we propose an evaluation on them from the view point of learning classification. Since there are a variety of models proposed within both technologies, we focus on the most significant model, i.e., Back Propagation Network (BPN) (J. McClelland et al., 1986) and Wang's fuzzy rule generator (L.X. Wang and J.M Mendel, 1992). First in the evaluation, we introduce a gravity effect field to illustrate these two models' influence under the existence of one instance. After that, we virtually construct two classification problems and discuss the behaviors of both methods through the gravity effect field. Finally, we propose another two real examples to demonstrate the results. We conclude that Wang's method is more suitable for piecewise region classification and needs more representative or complete training samples than BPN. BPN is more training data tolerant and less network parameter sensible than that of Wang's fuzzy rule generator. However, basic instinct problems still exist, BPN behavior is more black box than fuzzy rule generator.
神经网络和模糊逻辑就像一缕曙光洒进人工智能的云天，成为探索智能的尖端技术。为了对这两种技术进行判断，我们从学习分类的角度对它们进行了评价。由于在这两种技术中都提出了各种各样的模型，我们将重点放在最重要的模型上，即反向传播网络(BPN) (J. McClelland et al.， 1986)和Wang的模糊规则生成器(L.X. Wang和J.M Mendel, 1992)。在评价中，我们首先引入了一个重力场来说明在一个实例存在的情况下两种模型的影响。然后，我们虚拟构造了两个分类问题，并通过重力效应场讨论了两种方法的行为。最后，我们提出了另外两个真实的例子来证明结果。我们得出Wang的方法更适合于分段区域分类，并且比BPN需要更有代表性或完整的训练样本。与Wang的模糊规则生成器相比，BPN具有更强的训练数据容忍度和更低的网络参数感知能力。然而，基本本能问题仍然存在，BPN行为更多的是黑盒子而不是模糊规则生成器。
{"title":"Evaluation on neural network and fuzzy method-in terms of learning","authors":"Hung-Chang Lee, Tao Wang","doi":"10.1109/AFSS.1996.583561","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583561","url":null,"abstract":"Like a dawn light scattering into the cloud sky of AI, neural network and fuzzy logic become state-of-the-art technologies in exploring the intellect. To make a judgement between both technologies, we propose an evaluation on them from the view point of learning classification. Since there are a variety of models proposed within both technologies, we focus on the most significant model, i.e., Back Propagation Network (BPN) (J. McClelland et al., 1986) and Wang's fuzzy rule generator (L.X. Wang and J.M Mendel, 1992). First in the evaluation, we introduce a gravity effect field to illustrate these two models' influence under the existence of one instance. After that, we virtually construct two classification problems and discuss the behaviors of both methods through the gravity effect field. Finally, we propose another two real examples to demonstrate the results. We conclude that Wang's method is more suitable for piecewise region classification and needs more representative or complete training samples than BPN. BPN is more training data tolerant and less network parameter sensible than that of Wang's fuzzy rule generator. However, basic instinct problems still exist, BPN behavior is more black box than fuzzy rule generator.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186307","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}
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