Extreme value statistics using related variables

This dissertation contains five chapters that involve the use of the extreme value theory. Chapter 2 provides a novel methodology for improving the extreme value index estimation based on covariates in the case of heavy-tailed distributions. An application of the earthquakes and the related financial losses is used to show the improvement obtained when applying the proposed methodology. Chapters 3 and 4 introduce further generalizations for the proposed methodology in Chapter 2, by considering less assumptions, all cases for the extreme value index, and extending to the scale parameter and the estimation of the extreme quantile. In Chapter 3, An application of rainfall in France is used to demonstrate the use of the generalized methodology introduced in these two chapters. Chapter 5 combines the use of the extreme value theory with machine learning techniques. In Chapter 5, the machine learning technique is used to obtain an estimator of the Value-at-Risk (VaR) based on related covariates. An insurance application is used to show the effectiveness of the combined methodology of extreme value theory with machine learning

Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5)

Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites

This paper presents a spike-based model which employs neurons with functionally distinct dendritic compartments for classifying high dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron a capacity to perform a large number of input-output mappings. The model utilizes sparse synaptic connectivity; where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems and its performance is compared against that achieved using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Our proposed method attains comparable performance while utilizing 10 to 50% less computational resources than the other reported techniques.Comment: Accepted for publication in Neural Computatio

Gradient boosting with extreme-value theory for wildfire prediction

This paper details the approach of the team $\textit{Kohrrelation}$ in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine learning context with theoretically justified loss functions for gradient boosting. We devise a spatial cross-validation scheme and show that in our setting it provides a better proxy for test set performance than naive cross-validation. The predictions are benchmarked against boosting approaches with different loss functions, and perform competitively in terms of the score criterion, finally placing second in the competition ranking

Meminimalisasi Nilai Error Peramalan dengan Algoritma Extreme Learning Machine

This study uses a machine learning algorithm which is the extreme model one of the new learning method of neural networks. Determining the value of forecasting based on actual value. Output generated more quickly in this process, because the learning done in a fast speed and better accuracy rate than conventional forecasting methods. Epoch using the parameter changes and changes in the range of accuracy of test results demonstrate the value of error is quite good during the system testing.Keywords: neural networks, epoch, value of error