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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： J. Yen M.S. El-Nasr T.R. Ioerger Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

intelligent agents are software modules that can interact with its external environment in an autonomous fashion to achieve its goals. fuzzy logic can play a major role in an intelligent agent for reasoning about the dynamic environment. FLAME (fuzzy logic adaptive model of emotions) is a new computational model of emotions that can be incorporated into intelligent agents and other complex, interactive programs.

关键词： fuzzy logic intelligent agent Fires Control systems Decision making intelligent robots Space technology Space exploration Humans Artificial intelligence

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A framework for the choices of design alternatives

A framework for the choices of design alternatives

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： W.A. Tiao J. Yen Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

A software design process is to build a system that satisfies the customer's requirements which are often not crisply defined. Thus, design engineers often need to make design trade-off decisions to choose a set of design alternatives that satisfies the requirements as much as possible. In this paper, we propose a formal framework to assist design engineers in choosing design alternatives. Our approach uses fuzzy logic to represent quality requirements and design alternatives. Based on the representations, we can identify the impacts of design alternatives on requirements, infer the implicit impacts through hierarchical structures, and assist design engineers in the determination of a set of design alternatives. The goal of this research is to guide design engineers in evaluating design alternatives, to reduce design errors, to facilitate design rationale capture, and to build a system that better satisfies the customer.

关键词： Design engineering fuzzy logic Software design intelligent robots Software systems Natural languages Process design intelligent systems Computer science

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STAR: a tool for analyzing imprecise requirements

STAR: a tool for analyzing imprecise requirements

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： J. Yen W.A. Tiao Jianwen Yin Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

Requirement analysis is an important activity in a software development process. Customers usually describe requirements in a natural language, which is often vague. Requirements can be rarely satisfied in a black and white sense. That is, a requirement is usually satisfied to some degree. Existing requirement methodologies are limited in specifying requirements and in supporting tradeoff analysis between the conflicting requirements. A tool support for the requirement process is important since a number of tasks in the requirement process are very time consuming. We describe an on-going project for building a tool that supports requirement specification, trade-off, and analysis. In this tool, we use fuzzy logic to capture the imprecise requirements. Once customers identify some requirement relationships, we are able to infer more implicit relationships between requirements. This functionality is crucial since identification of all requirement relationships is tedious. Moreover, the tool provides the capability of making trade-off between conflicting requirements. We illustrate the tool using a textile spinning frame manufacturing example. The goal of developing such a tool is to automate part of the requirement process such that requirement errors can be reduced in the software development process.

关键词： fuzzy logic intelligent robots Programming Natural languages Software systems Formal specifications Robot sensing systems intelligent systems Computer science Textiles

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Simplification of fuzzy rule based systems using orthogonal transformation

Simplification of fuzzy rule based systems using orthogonal ...

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： J. Yen Liang Wang Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

It is known that removal of those redundant fuzzy rules from a rule base can result in a more compact fuzzy model with better generalizing ability. In this paper we propose a number of orthogonal transformation based methods which provide new or alternative tools for rule extraction. A common attribute of these methods is that they all work on a truth value matrix and employ some measure index to detect the rules that should be retained and eliminated. The performance of these methods is illustrated using a nonlinear plant modeling example.

关键词： fuzzy systems Knowledge based systems fuzzy logic intelligent robots Input variables intelligent systems Computer science Least squares methods Singular value decomposition State-space methods

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A simplex genetic algorithm hybrid

A simplex genetic algorithm hybrid

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IEEE International Conference on Evolutionary Computation

作者： John Yen Bogju Lee Center for Fuzzy Logic Robotics Intelligent Systems Department of Computer Sciences Texas A and M University College Station TX USA

ISBN: (纸本)0780339495

One of the main obstacles in applying genetic algorithms (GAs) to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques, including another simplex-GA hybrid, developed independently by Renders and Bersini (1994), and adaptive simulated annealing (ASA). We used two function optimization problems to compare our approach with the five alternative methods. Overall, these tests showed that our hybrid approach is an effective and robust optimization technique. We also tested our hybrid GA on the seven function benchmark problems on real space and showed its results.

关键词： Genetic algorithms Optimization methods Convergence Stochastic processes Simulated annealing Benchmark testing Computational efficiency intelligent robots Costs Robustness

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A global-local learning algorithm for identifying Takagi-Sugeno-Kang fuzzy models

A global-local learning algorithm for identifying Takagi-Sug...

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： J. Yen Liang Wang W. Gillespie Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

The fuzzy inference system proposed by Takagi, Sugeno and Kang, which is known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multi-model approach in which simple submodels combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance, they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior which is difficult to interpret. Since one of the important motivations of using the TSK model is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. We propose a learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighted regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user's preference, generating models with good trade-off in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example.

关键词： Takagi-Sugeno-Kang model Power system modeling fuzzy systems Inference algorithms Training data fuzzy logic intelligent robots Nonlinear systems Approximation algorithms fuzzy control

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Multiple fuzzy systems for function approximation

Multiple fuzzy systems for function approximation

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Conference of the North American fuzzy Information Processing Society - NAFIPS

作者： J. Yen Liang Wang R. Langari Center for Fuzzy Logic Roboticsand Intelligent Systems Department of Computer Science Texas A and M University College Station TX USA

The standard procedure for building a fuzzy model often involves trying several candidates with varying number of fuzzy rules and training parameters in order to achieve acceptable model accuracy. Typically, one of the candidates is chosen as best, while the rest are discarded. When the system being considered is highly nonlinear or includes a number of input variables, the number of fuzzy rules constituting the underlying model is usually large. The paper proposes an alternative approach to designing fuzzy systems. The essential scheme is to decompose the overall system into subsystems and then combine their individual outputs. This offers advantages of speed, reliability, and simplicity of design. The concept of competition developed in modular networks theory is used to derive an identification algorithm. The utility of the proposed approach is illustrated by a nonlinear function approximation example.

关键词： fuzzy systems Function approximation Neurons Computer networks Neural networks fuzzy neural networks

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An Adaptive fuzzy Controller with Application to Petroleum Processing

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IFAC Proceedings Volumes 1992年第28期25卷 327-332页

作者： John Yen Haojin Wang Walter C. Daugherity Center for Fuzzy Logic and Intelligent Systems Research Department of Computer Science Texas A&M University College Station TX 77843-3112

fuzzy logic controllers have some oftencited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systenlatic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues re lated to the design of a SFLC for application to a petrochemical process are discussed and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

关键词： fuzzy control intelligent control process control self-adapting systems learning systems petrochemical process

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Application of fuzzy logic for detecting incidents at signalized highway intersections

Application of fuzzy logic for detecting incidents at signal...

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： S. Lee J. Yen R.A. Krammes Texas A&M ITS Research Center of Excellence Texas A and M University USA Center for Fuzzy Logic Robotics and Intelligent Systems Texas A and M University USA

Traffic incidents-accidents, cargo spills, and stalled vehicles-are a major cause of urban congestion. Research in incident detection for signalized arterial streets is at a very initial stage. Existing algorithms are far from being robust in dealing with the difficulties related to data availability and the multi-dimensional nature of the incident detection problem. This paper discusses a fuzzy-logic based incident detection model for real-time implementation at signalized diamond interchanges. The model overcomes the boundary condition problem inherent in conventional threshold-based methods. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and is incorporated into a real-time traffic adaptive diamond interchange control system. The model is scaleable and expandable for larger systems of arterial streets. Simulation results suggest that the fuzzy-logic-based approach to incident detection at signalized diamond interchanges is promising.

关键词： fuzzy logic Signal detection Road transportation Traffic control Real time systems Control systems Adaptive control Vehicle detection intelligent robots Road vehicles

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fuzzy logic as a basis for specifying imprecise requirements

Fuzzy logic as a basis for specifying imprecise requirements

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

作者： J. Yen J. Lee Center for Fuzzy Logic and Intelligent Systems Research Department of Computer Scienc Texas A and M University TX USA

A major challenge with requirements engineering is that the requirements to be captured usually are described in qualitative terms which are imprecise in nature. The authors propose to use soft functional requirements in a task-based specification methodology to capture the imprecise requirements, and to formulate soft functional requirements using fuzzy logic. More specifically, the soft conditions are represented using the canonical form in test-score semantics. The proposed approach not only can facilitate the exploration of various tradeoffs during the design phase, but also can enable a more realistic validation of the user's imprecise requirements.< >

关键词： fuzzy logic System testing Expert systems intelligent systems Computer science Software safety Formal specifications Real time systems Qualifications Elasticity

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