Robust control of nonlinear stochastic systems by modelling conditional distributions of control signals

We introduce a novel inversion-based neuro-controller for solving control problems involving uncertain nonlinear systems that could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. In this work a novel robust inverse control approach is obtained based on importance sampling from these distributions. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The performance of the new algorithm is illustrated through simulations with example systems

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data

Optimasi Radial Basis Function Neural Network dengan Growing Hierarchial Self Organizing Map pada Data Time Series

Salah satu model JST yang sesuai dengan peramalan data time series, adalah model Radial Basis Function Network (RBFN). Jaringan syaraf tiruan Radial Basis Function merupakan jaringan feed-forward yang memiliki tiga lapisan, yaitu lapisan masukan (input layer), lapisan tersembunyi (hidden layer) dan lapisan keluaran (output layer). Besarnya dimensi input pada jaringan syaraf menyebabkan menurunnya kemampuan komputasi suatu model jaringan. Salah satu cara untuk mengatasi hal tersebut adalah dengan mereduksi dimensi input. Dalam penelitian ini jaringan syaraf tiruan Radial Basis Function dipadukan dengan metode Growing Hierarchical Self Organizing Map (GH-SOM). Penggunaan teknik clustering data pada proses awal, memungkinkan mengurangi dimensi input dengan kehilangan informasi yang minimum. Sehigga dapat mengoptimalkan proses prediksi dengan menggunakan pendekatan RBFN. Prediksi harga saham dengan Optimasi metode Radial Basis Function neural network dengan Menggunakan Growing Hierarchical Self Organizing Map, dengan jumlah vektor data sebanyak 364 dengan SSE sebesar 0,074713 diperoleh akurasi sebesar 94,03

Fault-Tolerant Optimal Neurocontrol for a Static Synchronous Series Compensator Connected to a Power Network

This paper proposes a novel fault-tolerant optimal neurocontrol scheme (FTONC) for a static synchronous series compensator (SSSC) connected to a multimachine benchmark power system. The dual heuristic programming technique and radial basis function neural networks are used to design a nonlinear optimal neurocontroller (NONC) for the external control of the SSSC. Compared to the conventional external linear controller, the NONC improves the damping performance of the SSSC. The internal control of the SSSC is achieved by a conventional linear controller. A sensor evaluation and (missing sensor) restoration scheme (SERS) is designed by using the autoassociative neural networks and particle swarm optimization. This SERS provides a set of fault-tolerant measurements to the SSSC controllers, and therefore, guarantees a fault-tolerant control for the SSSC. The proposed FTONC is verified by simulation studies in the PSCAD/EMTDC environment

An Innovative Learning-by-Example Methodological Strategy for Advanced Reflectarray Antenna Design

Reflectarray antennas are reflector structures which combine characteristics of both reflector and array antennas. They exhibit electrically large apertures in order to generate significant gain as conventional metallic reflector antennas. At the same time they are populated by several radiating elements which can be controlled individually like conventional phased array antennas. They are usually flat and can be folded and deployed permitting important saving in terms of volume. For these reasons they have been considered since several years for satellite applications. Initially constituted by truncated metallic waveguides and mainly considered for radar applications, they are now mainly constituted by a dielectric substrate, backed by a metallic plane (groundplane) on which microstrip elements with variable shape/size/orientation are printed. These elements are illuminated by the primary feed. The reflected wave from each element has a phase that can be controlled by the geometry of the element itself. By a suitable design of the elements that make up the reflectarray, it is therefore possible to compose the phase front of the reflected waves in the desired direction (steering direction), and to ensure that the obtained overall radiation pattern exhibits a secondary lobe profile which meets the design specifications. Reflectarrays may be used to synthesize pencil or shaped beams. The synthesis methods commonly used to achieve this goal are based on three different steps: (a) calculation of the nearfield “phase distribution” that the wave reflected by the reflectarray must exhibit to get the desired far-field behaviour; (b) discretization of such distribution into cells of size comparable to that of the elements of interest (i.e., the patches); (c) calculation of the geometry of each elementary cell that will provide the desired reflection coefficient. The first step (a) is a Phase Only approach and permits already to achieve fast preliminary indications on the performance achievable. Accurate results require the implementation of the steps (b) and (c) as well and it is thus of fundamental importance to have techniques capable of efficiently and accurately calculating the reflection coefficient associated with a given geometry of the element [in order to efficiently solve the step (c)]. This coefficient is mathematically represented by a 2x2 complex matrix, which takes into account the relationships between co-polar and cross-polar components of the incident (due to the feed) and reflected field. This matrix naturally depends on the geometry of the element, the direction of incidence of the wave (azimuth and elevation) and the operating frequency of the system. The computation of the reflection coefficient is usually performed using electromagnetic full-wave (FW) simulators; the computation is however time consuming and the generation of the unit cells scattering response database becomes often unfeasible. In this work, an innovative strategy based on an advanced statistical learning method is introduced to efficiently and accurately predict the electromagnetic response of complex-shaped reflectarray elements. The computation of the scattering coefficients of periodic arrangements, characterized by an arbitrary number of degrees-of-freedom, is firstly recast as a vectorial regression problem, then solved with a learning-by-example strategy exploiting the Ordinary Kriging paradigm. A set of representative numerical experiments dealing with different element geometries is presented to assess the accuracy, the computational efficiency, and the flexibility of the proposed technique also in comparison with state-of-the-art machine learning methods

Connectivity analysis from EEG phase synchronisation in emotional BCI

A Brain Computer Interface (BCI) is a device that uses the brain activity of the user as an input to the system to select the desired output on a computer, giving the person a different pathway to establish communications with the surrounding environment. There are many types and uses of BCIs. They can be defined by which technique is used to record the brain activity of the user and which variety of stimuli is used to trigger a consistent response from the user, following the signal processing methodology selected to produce a response on the computer. Each one of the selected choices will determine the reliability and efficiency of the BCI system. However, even with this flexibility, the performance of BCI systems used for assistive technology or rehabilitation processes still remains behind other systems and the percentage of people unable to use one of these systems remains too high. The main objective of this thesis is to improve the classification performance and reliability of the current electroencephalogram (EEG) based BCI systems. Firstly, a novel paradigm based on emotional faces is used with the aim of enhancing a stronger response from the user, therefore a higher amplitude of brain activity. Two types of emotional faces have been used during this work. Initially, emotional schematic faces or emotions were used. Posteriorly, human emotional faces were introduced into the experiments. Additionally, the evolution of the phase synchronisation over time is studied to achieve a deeper understanding of the latent communication mechanisms of the different parts of the human brain. Wavelet families and their ability to retain temporal and frequency information simultaneously have been used to study the phase relationships between the EEG signals when a specific task is being performed. This study has led to the identification of a reduced number of discrete states with a quasi-stable phase synchronisation of the order of milliseconds, named synchrostates. Those synchrostates present switching patterns over time, clearly distinctive for each one of the tasks performed by the user. In order to establish a classification protocol the temporal stability of each task-specific synchrostate was studied by means of the synchronisation index and posteriorly translated into connectivity network maps based on graph theory. From this connectivity network, a series of connectivity metrics was obtained and used to feed a variety of classification algorithms. This process led to accuracies of 83% for a two-tasks classification problem and rose to a 93% averaged accuracy for a four-tasks problem

PROSIDING SEMINAR NASIONAL INOVASI TEKNOLOGI DAN ILMU KOMPUTER (SNITIK 2018)

pada kegiatan ini mangadakan seminar nasional inovasi teknologi dan ilmu komputer snitik 2018