Multiple model control and maximum entropy control of flexible structures

implementation and evaluation.
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by
The Physical Object
Pagination178 p.
ID Numbers
Open LibraryOL20200914M
ISBN 100612587215

What is meant by maximum entropy. If we had a fair coin like the one shown below where both heads or tails are equally likely, then we have a case of highest uncertainty in predicting outcome of a toss - this is an example of maximum entropy In co.

Recent years have seen increasing research in active control of flexible structures. The primary motivation for this research is control of large flexible aerospace structures, which are becoming larger and more flexible at the same time that their performance requirements are.

By working within the framework of maximum entropy theory and utilizing a flexible object-based architecture, the system is able to make use of an extraordinarily diverse range of knowledge. stochastic model (Neu & SzepesvriRamachandran & Amir ), learn-ing in our model is convex, cannot get “stuck” in local maxima, and provides performance guarantees.

Probabilistic Graphical Models A great deal of research within the machine learning community has focused 3 Maximum Entropy Inverse Optimal Control 5.

Download Multiple model control and maximum entropy control of flexible structures EPUB

The control design of each local controller is achieved using internal model control structure. An illustrative example shows the application of the proposed control structure to a container ship.

The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge is the Multiple model control and maximum entropy control of flexible structures book with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).

Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. A Minimum Entropy Approach for Multiple-model Estimation Han Shen-Tu State Key Laboratory of Industrial Control Technology Institute of Cyber-System and Control Zhejiang University, Hang Zhou,P.R.

China Email: [email protected] AnKe Xue and DongLiang Peng Institution of Information and Control Hangzhou Dianzi University, Hang Zhou. This report demonstrates the use of a particular maximum entropy model on an example problem, and then proves some relevant mathematical facts about the model in a simple and accessible manner.

This report also describes an existing procedure calledGeneralized Iterative Scaling, which estimates the parameters of this particular by: We then choose the maximum entropy distribution which otherwise accords with these constraints.

Berger et al. () show that the solution to this optimization problem turns out to be exactly the probability distribution of a multinomial logistic regression model whose weights W maximize the likelihood of the training data.

based on the maximum entropy philosophy. In Section 2 we give an overview of the maximum entropy philosophy and work through a motivating example. In Section 3 we describe the mathematical structure of maximum entropy models and give an efficient algorithm for estimating the parame- ters of such models.

Maximum Entropy and Log-linear Models 14/29 € Representing Evidence Constraint: observed expectation of each feature has to be the same as the model’s expectation of the feature: E pf j = E p f • Maximum-entropy properties of log-linear models. Human Behavior Modeling with Maximum Entropy Inverse Optimal Control Brian D.

Ziebart, Andrew Maas, Bagnell, and Anind K. Dey School of Computer Science Carnegie Mellon University Pittsburgh, PA [email protected], [email protected], [email protected], [email protected] Abstract In our research, we view human behavior. a major thrust in the extension of modern control theory and the emerging cross-discipline technology of active structural control covers a wide range of interrelated design and system functions.

As depicted below, the general structure control methodology is to phase stabilize modes lying within the control bandwidth (in-band modes) and.

An application of instantaneous optimal control algorithm using the Newmark integration scheme to flexible structures is presented. The proposed control design procedure involves only a few simple algebraic manipulations, and this differs from the Riccati equation approach in which one needs to solve a large dimension of eigenvalue/eigenvector by: 1.

of the maximum entropy method which provides a mean to obtain least-biased pdf estimator [19]. The maximum entropy problem for a continuous random variable, such as electricity price, introduces a functional optimization problem. Thus, in this paper the structure of optimal control problem is utilized to model entropy.

Robustness and Performance Tradeoffs in Control Design for Flexible Structures Gary J. Balas Dept. of Aerospace Engineering and Mechanics John C. Doyle Dept. of Electrical Engineering University of hlinnesota Minneapolis, MN Abstract Linear control design methodologies for flexible structures require the nominal structural model to be.

To this end, the minimum entropy multiple-model (MEMM) algorithm is proposed as it directly calculates the effective model sequence set in the absence of the basic model set.

23 In the MEMM, the posterior entropies of all candidate model sequence sets are computed through a feedback structure and the minimum entropy model sequence set is Cited by: 3. This thesis considers the control of multiple model systems.

These are systems for which only one model out of some finite set of models gives the system dynam-ics at any given time.

In particular, the model that gives the system dynamics can change over time. This thesis covers some of the theoretical aspects of these.

Maximum Entropy The main idea behind maximum entropy principle is that unknown model generating the sample data should be the model that is most uniform and satisfy all constrains from sample data (or training data).

For example, given a document d and three classes c1, c2, c3. If we know nothing from sample data, in otherFile Size: KB. The paper presents some aspects about the control of processes with important structure variations during their operation.

The adopted solution is based on a multiple model structure. A solution for the switching strategy is proposed and its applicability is analyzed using a real-time structure implementation based on a RST control : Cuprian Lupu, Alexandru Ticlea, Catalin Petrescu, Silviu Cirstoiu.

SharpEntropy, like its Java original, holds all of the maximum entropy model data in memory at once, and complex models (such as those used by OpenNLP) may easily consume hundreds of MB.

Description Multiple model control and maximum entropy control of flexible structures EPUB

I have been looking into the possibility of keeping most of the model in permanent storage and only accessing it when necessary/5(42). 4 Maximum Entropy In the equivalent maximum entropy derivation of logistic regression you don’t have to cleverly guess the sigmoid form.

Instead you assume you want a balance equation like equation15to be true and you can, without needing any luck, solve for the necessary form for ˇ(): Start as by: 7.

Maximum-Entropy distributions obtained by specifying covariances Maximum-Entropy distributions when sumof variates is a constant Maximum-Entropy distributions obtained by also specifying expected values of functions of all variates 5 4 Maximum-Entropy distribution obtained by finding distributions.

Skelton, "Control Design of Flexible Spacecraft," Chapter 8, Theory and Applications of Optimal Control in Aerospace Systems, ed. Kant, AGARD publication no. KBNJuly Distributed in the United States, by NASA, Langley Field, Va.attn: Report Distribution and Storage by: 5.

The modeling, planning and control of realistic flexible robots represent a research area with significant problems to face, like, for example, the followings: • the derivation of a model which is precise under the assumption of almost static motion. The concept of maximum entropy can be traced back along multiple threads to Biblical times.

Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. The maximum entropy approach to phonotactics, like many others, is based on phonological constraints.1 Crucially, however, it makes no commitments about the content of these constraints, leaving this as a question of phonological theory.

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Moreover, as we will show, maximum entropy models can assess well-formedness using cross-classifying principles. tions based on other norms. This paper will focus on conditional maximum entropy models with L2 regularization. These models have been extensively used and studied in natural language processing [1, 3] and other areas where they are typically used for classification.

Given the weight vector w, the output y predicted by the model for an input File Size: KB. SCOPE This website concerns the theory and application of the maximum entropy (MaxEnt) method, for the analysis of probabilistic systems of all method is based on the concept of entropy, one of the most fundamental discoveries of human the MaxEnt method, we maximize the (relative) entropy of a system, subject to its constraints, to infer the state of the.

Conceptual models are important tools for understanding and predicting the hydrologic behavior of meso-scale catchments [] and the identification of an appropriate model structure is often seen as a means to characterize catchment functioning, e.g., [4,5,6].Different authors have criticized the use of off-the-shelf models in favor of “flexible” approaches that can be adapted to the Cited by: 3.

The MaxEnt is based on the Principle of Maximum Entropy and from all the models that fit our training data, selects the one which has the largest entropy. The Max Entropy classifier can be used to solve a large variety of text classification problems such as language detection, topic classification, sentiment analysis and more.The equivalence of logistic regression and maximum entropy models Nina Zumel recently gave a very clear explanation of logistic regression (The Simpler Derivation of Logistic Regression).

In particular she called out the central role of log-odds ratios and demonstrated how the “deviance” (that mysterious.Model Based Vibration Control of This paper focuses on development and implementation of optimal control algorithms for vibration control of flexible beam structures with embedded piezoelectric actuators.

Piezoelectric transducers have become the leading maximum actuation strain, reasonable cost, and high accessibility [6].Cited by: 1.