Speed control of dc motor using fuzzy logic thesis

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Speed control of dc motor using fuzzy logic thesis

They can be used for non-linear regression, time-series modelling, classification, and many other problems. Streaming sparse Gaussian process approximations. Sparse approximations for Gaussian process models provide a suite of methods that enable these models to be deployed in large data regime and enable analytic intractabilities to be sidestepped.

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However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion.

The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive.

This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing principled methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is experimentally validated using synthetic and real-world datasets.

The first two authors contributed equally. The unreasonable effectiveness of structured random orthogonal embeddings. We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.

We introduce matrices with complex entries which give significant further accuracy improvement. We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

We present a data-efficient reinforcement learning method for continuous state-action systems under significant observation noise.

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Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s. PILCO evaluates policies by planning state-trajectories using a dynamics model. This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original unfiltered PILCO algorithm.

We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control.

Speed control of dc motor using fuzzy logic thesis

Skoglund, Zoran Sjanic, and Manon Kok. On orientation estimation using iterative methods in Euclidean space. This paper presents three iterative methods for orientation estimation. The third method is based on nonlinear least squares NLS estimation of the angular velocity which is used to parametrise the orientation.

The Multivariate Generalised von Mises distribution: Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community.

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This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First we introduce a new multivariate distribution over circular variables, called the multivariate Generalised von Mises mGvM distribution.

This distribution can be constructed by restricting and renormalising a general multivariate Gaussian distribution to the unit hyper-torus. Previously proposed multivariate circular distributions are shown to be special cases of this construction.

Second, we introduce a new probabilistic model for circular regression, that is inspired by Gaussian Processes, and a method for probabilistic principal component analysis with circular hidden variables. These models can leverage standard modelling tools e.

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Third, we show that the posterior distribution in these models is a mGvM distribution which enables development of an efficient variational free-energy scheme for performing approximate inference and approximate maximum-likelihood learning.

A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation. Gaussian processes GPs are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way.

Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models.

Consequently, a wealth of GP approximation schemes have been developed over the last 15 years to address these key limitations. Many of these schemes employ a small set of pseudo data points to summarise the actual data.Other Details: Research Interest.

Complex II has been extensively studied for its dual functionality in Krebs cycle and oxidative phosphorylation.

Electrical Engineering and Computer Science (EECS) spans a spectrum of topics from (i) materials, devices, circuits, and processors through (ii) control, signal processing, and systems analysis to (iii) software, computation, computer systems, and networking.

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Speed control of dc motor using fuzzy logic thesis

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Boumedience Allaoua, Abdellah Laoufi, Brahim Gasbaoui, Abdelfatah Nasri and Abdessalam Abderrahmani, “Intelligent Controller Design for DC motor Speed control Based on Fuzzy Logic-Genetic Algorithms Optimization”, Leonardo Journal of Science, vol.

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