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ANFIS: AdaptiveNetworkbased Fuzzy Inference Systems
 IEEE Transactions on Systems, Man, and Cybernetics
, 1993
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Qualitative Simulation
 Artificial Intelligence
, 2001
"... Qualitative simulation predicts the set of possible behaviors... ..."
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Cited by 513 (31 self)
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Qualitative simulation predicts the set of possible behaviors...
A survey of industrial model predictive control technology
, 2003
"... This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control an ..."
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Cited by 437 (5 self)
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This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented first, followed by results of our vendor survey of MPC control and identification technology. A general MPC control algorithm is presented, and approaches taken by each vendor for the different aspects of the calculation are described. Identification technology is reviewed to determine similarities and differences between the various approaches. MPC applications performed by each vendor are summarized by application area. The final section presents a vision of the next generation of MPC technology, with an emphasis on potential business and research opportunities.
Dynamic Textures
, 2002
"... Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing ..."
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Cited by 373 (18 self)
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Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include seawaves, smoke, foliage, whirlwind etc. We present a novel characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the "essence" of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of secondorder stationary processes, we identify the model suboptimally in closedform. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even lowdimensional models can capture very complex visual phenomena.
Learning the structure of dynamic probabilistic networks
, 1998
"... Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when so ..."
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Cited by 284 (14 self)
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Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains. 1
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 231 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
Nonlinear BlackBox Modeling in System Identification: a Unified Overview
 Automatica
, 1995
"... A nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area with structures based on neural networks, radial basis networks, wavelet networks, hinging hyperplanes, ..."
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Cited by 213 (15 self)
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A nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area with structures based on neural networks, radial basis networks, wavelet networks, hinging hyperplanes, as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping from observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function e...
Motion Texture: A TwoLevel Statistical Model for Character Motion Synthesis
 ACM Transactions on Graphics
, 2002
"... In this paper, we describe a novel technique, called motion texture, for synthesizing complex humanfigure motion (e.g., dancing) that is statistically similar to the original motion captured data. We de fine motion texture as a set of motion textons and their distribution, which characterize the s ..."
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Cited by 213 (2 self)
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In this paper, we describe a novel technique, called motion texture, for synthesizing complex humanfigure motion (e.g., dancing) that is statistically similar to the original motion captured data. We de fine motion texture as a set of motion textons and their distribution, which characterize the stochastic and dynamic nature of the captured motion. Specifically, a motion texton is modeled by a linear dynamic system (LDS) while the texton distribution is represented by a transition matrix indicating how likely each texton is switched to another. We have designed a maximum likelihood algorithm to learn the motion textons and their relationship from the captured dance motion. The learnt motion texture can then be used to generate new animations automatically and/or edit animation sequences interactively. Most interestingly, motion texture can be manipulated at different levels, either by changing the fine details of a specific motion at the texton level or by designing a new choreography at the distribution level. Our approach is demonstrated by many synthesized sequences of visually compelling dance motion.
Linear Controller Design: Limits of Performance Via Convex Optimization
, 1990
"... this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors, whic ..."
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Cited by 190 (25 self)
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this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors, which process the measu red signals to drive actuators, which affect the behav ior of the system. A schematic diagram of a general control system is shown in Fig. 1. The use of the sensed response of the system (and not just the command signals) in the computation of the actuator signals is called feedback control, an old idea which has been developed and applied with great success in this century [1], [2]. Control engineering involves 1. Modeling or identification. The designer develops mathematical models of the relevant aspects of system to be controlled. This can be done using knowl edge of the system (for example by applying Newton 's equations of motion to a mechanical system), and experimentally by observing responses of the Manuscript received November 30,1988; revised August 4,1989. This work was supported in part by the National Science Foundation (NSF) under ECS8552465, the Air Force Office of Scientific Research (AFOSR) under 890228, Boeing Electronics Company under LF0937, and Bell Communications Research, and the National Science and Engineering Research Council (Canada) 1967 Science and Engineering Scholarship. The authors arewith the Dept. of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. IEEE Log Number 8933936. system to various excitations, a procedure known as s)zstem identification [3]. In some cases, several models are developed, varying in complexity and accu racy. 2. Control configuration: selection and placement of sensors an...
Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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Cited by 160 (3 self)
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.