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Radial basis function networks, regression weights, and the expectation-maximization algorithm

IEEE TRANSACTIONS ON systems MAN AND CYBERNETICS PART A-systems AND HUMANS 1997年第5期27卷 613-623页

作者： Langari, R Wang, L Yen, J Center for Fuzzy Logic Robotics and Intelligent Systems Texas A and M University College Station TX USA

We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer, It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights, A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units, The superior performance of the modified RBF network over the standard RBF network is illustrated by computer simulations.

关键词： Radial basis function networks Expectation-maximization algorithms Signal processing algorithms Iterative algorithms intelligent robots Parameter estimation Computer simulation Process control Biomedical signal processing Pattern recognition

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A fuzzy logic-BASED METHODOLOGY FOR THE ACQUISITION AND ANALYSIS OF IMPRECISE REQUIREMENTS

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 1994年第4期2卷 265-277页

作者： YEN, J LIU, XQ TEH, SH [o_1]Center for Fuzzy Logic and Intelligent Systems Research Department of Computer Science Texas A&M University College Station Texas 77843-3112 USA [o_2]Center for Fuzzy Logic and Intelligent Systems Research Department of Computer Science Texas A&M University College Station Texas 77843-3112 USA [o_3]Center for Fuzzy Logic and Intelligent Systems Research Department of Computer Science Texas A&M University College Station Texas 77843-3112 USA

Two major challenges with requirement analysis in concurrent engineering are: (1) requirements from multiple members of a concurrent engineering team are often conflicting with each other;and (2) requirements are often imprecise in nature. Existing formal methods for requirement engineering are very limited in addressing these issues. More specifically, they have not fully explored the use of artificial intelligence technique for achieving effective trade-offs among conflicting imprecise requirements. This paper presents a comprehensive methodology for specifying imprecise requirements and for characterizing complex relationships among them to facilitate trade-off analysis. Imprecise requirements are represented by the canonical form in test-score semantics in fuzzy logic. A formal approach and a practical method are developed to analyze the complex relationships between requirements. Conflicting requirements can be identified and represented using both qualitative terms and quantitative measures. Multiple requirements with complex relationships among them are fused into an overall system requirement based on fuzzy multi-criteria decision techniques. To obtain a feasible overall system requirement that is satisfactory to customers, the iterative refinement of requirements and the negotiation between the customers and the requirement analysts regarding conflicting requirements are crucial. Our methodology supports the iterative process of refinement end negotiation by facilitating a formal trade-off analysis, by providing intelligent feedbacks generated based on the analysis, and by defining a clear process of compromise. Therefore, this methodology can help to achieve a better system objective that is satisfactory to customers and feasible to developers by fully exploiting the elasticity of imprecise requirements. In addition, the explicit specification of imprecise requirements provides a basis for verification and validation of software systems.

关键词： REQUIREMENT ENGINEERING ARTIFICIAL INTELLIGENCE fuzzy logic IMPRECISE REQUIREMENT CONFLICTING REQUIREMENTS NEGOTIATION CONCURRENT ENGINEERING TRADE-OFF ANALYSIS FORMAL METHOD MULTICRITERIA DECISION MAKING

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A framework for the choices of design alternatives

A framework for the choices of design alternatives

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1998 IEEE International Conference on fuzzy systems, fuzzy 1998

作者： Tiao, W. Amos Yen, John Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College StationTX77843-3112 United States

ISBN: (纸本)078034863X

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. © 1998 IEEE.

关键词： fuzzy logic

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Constructing optimal fuzzy models using statistical information criteria 1

Constructing optimal fuzzy models using statistical informat...

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1996 Mexico-USA Collaboration in intelligent systems Technologies - 1st Joint Conference on intelligent systems between the 9th International Symposium on Artificial Intelllgence, ISAI 1996 and the 6th International Conference on Industrial fuzzy Control and intelligent systems, IFIS 1996

作者： Yen, J. Liang Wang Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas AandM University College StationTX77843-3112 United States

ISBN: (纸本)9682994373

In this paper we describe a principled approach to explore the fitness-complexity trade-off using several optiniality criteria together with a fuzzy model reduction technique based on the singular value decomposition (SVD). The role of these optimality criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example. © 1996 ITESM.

关键词： Economic and social effects

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STAR: A tool for analyzing imprecise requirements

STAR: A tool for analyzing imprecise requirements

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1998 IEEE International Conference on fuzzy systems, fuzzy 1998

作者： Yen, John Tiao, W. Amos Yin, Jianwen Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas A and M University College StationTX77843-3112 United States

ISBN: (纸本)078034863X

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. © 1998 IEEE.

关键词： fuzzy logic

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Incorporating personality into a multi-agent intelligent system for training teachers 4th

Incorporating personality into a multi-agent intelligent sys...

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4th International Conference on intelligent Tutoring systems, ITS 1998

作者： Yin, Jianwen El-Nasr, Magy Seif Yang, Linyu Yen, John Center for Fuzzy Logic Robotics and Intelligent Systems Department of Computer Science Texas AandM University College StationTX77843-3112 United States

ISBN: (纸本)3540647708

This paper presents a system prototype for training a teacher based on two objects: the topic and students. In the system prototype, a concept model is defined to provide many topics needed to train teachers. Moreover, the Virtual Classroom Environment (VCE) is built to provide a multi-agent environment for teaching. In the VCE, students with many different personalities and motivations can communicate with each other as well as the teacher. The teacher can use various teaching techniques provided by the system to teach students specific topics in the VCE. The teacher can also test the students at the end of each class. Furthermore, the teacher can also ask the system to give an evaluation of the teaching process. The system will evaluate the user based on the result of the test(s) and the statistics of the whole teaching process throughout the class. The user can gain his/her teaching experience by analyzing the evaluation. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Students

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A fuzzy logic Based Robot Navigation System

A Fuzzy Logic Based Robot Navigation System

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1992 AAAI Fall Symposium on Applications of AI to Real-World Autonomous Mobile Robots

作者： Yen, John Pfluger, Nathan Center for Fuzzy Logic and Intelligent Systems Department of Computer Science Texas A&MUniversity College StationTX77843 United States

One of the key issues in mobile robot navigation is coping with uncertainty in a dynamic environment. One possible solutions lies in behaviors, but there is a problem with standard behavior arbitration techniques. To combat this problem, David Payton introduced a commandarbitration net that minimizes the loss of information between behaviors. Wehave extended his approach using fuzzy logic. Somebenefits of using fuzzy logic include simplicity, extensibility and understandability, especially in the area of sensor fusion. Copyright © 1992, AAAI (***). All rights reserved.

关键词： fuzzy logic

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A formal framework for the impacts of design strategies on requirements

A formal framework for the impacts of design strategies on r...

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Proceedings of the Asian fuzzy systems Symposium, "Soft Computing in intelligent systems and Information Processing"

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

One of the important tasks in requirements engineering is to identify the impacts of various design strategies on satisfying the requirements of the target system. This paper proposes a formal framework for studying the relationships between design strategies and requirements. The elasticity of imprecise requirements is captured using fuzzy logic, and design strategies are explicitly defined in terms of requirements. Based on such a formal framework, we have developed a systematic reasoning scheme for reasoning about the positive/negative impacts of design strategies on requirements. The framework can reduce the efforts in identifying requirement and design conflicts and assist the software engineers in evaluating design alternatives more effectively.

关键词： Design engineering fuzzy logic intelligent robots Elasticity Software maintenance Maintenance engineering Reliability engineering Process design intelligent systems Computer science

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Constructing optimal fuzzy models using statistical information criteria

Constructing optimal fuzzy models using statistical informat...

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Proceedings of ISAI/IFIS Mexico-USA Collaboration in intelligent systems Technologies

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

关键词： Training data Probability distribution fuzzy logic intelligent robots fuzzy systems Solid modeling intelligent systems Computer science Reduced order systems Singular value decomposition

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An SVD-based fuzzy model reduction strategy

An SVD-based fuzzy model reduction strategy

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IEEE International Conference on fuzzy systems (FUZZ-IEEE)

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

This paper describes a novel fuzzy model reduction approach for overcoming the curse of dimensionality associated with high-dimensional data modeling problems. A numerically reliable orthogonal transformation technique, known as the singular value decomposition (SVD), is utilized to detect and select the dominant fuzzy rules from a rule base. The effectiveness of the proposed approach is illustrated using a nonlinear limit cycle modeling problem.

关键词： Reduced order systems Input variables fuzzy systems fuzzy logic intelligent robots Singular value decomposition fuzzy control Spline intelligent systems Computer science

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