New hybrid control architecture for intelligent mobile robot navigation in a manufacturing environment

U radu je prikazana nova hibridna upravljačka arhitektura namenjena za eksploataciju i navigaciju inteligentnih mobilnih robota u tehnološkom okruženju. Arhitektura je bazirana na empirijskom upravljanju i implementaciji koncepta mašinskog učenja u vidu razvoja sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja mobilnog robota. Za razliku od konvencionalne metodologije razvoja inteligentnih mobilnih robota, predložena arhitektura je razvijena na temeljima eksperimentalnog procesa i implementacije sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja. Predložena metodologija razvoja i implementacije inteligentnih mobilnih robota treba da omogući nesmetanu i pouzdanu eksploataciju ali i robustnost u pogledu generisane upravljačke komande, kao odgovora robota na trenutno stanje tehnološkog okruženja.This paper presents a new hybrid control architecture for Intelligent Mobile Robot navigation based on implementation of Artificial Neural Networks for behavior generation. The architecture is founded on the use of Artificial Neural Networks for assemblage of fast reacting behaviors, obstacle detection and module for action selection based on environment classification. In contrast to standard formulation of robot behaviors, in proposed architecture there will be no explicit modeling of robot behaviors. Instead, the use of empirical data gathered in experimental process and Artificial Neural Networks should insure proper generation of particular behavior. In this way, the overall architectural response should be flexible and robust to failures, and consequently provide reliableness in exploitation. These issues are important especially if one takes under consideration that this particular architecture is being developed for mobile robot operating in manufacturing environment as a component of Intelligent Manufacturing System

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

Comparative analysis of stock selection using a hybrid MCDM approach and modern portfolio theory

The problem of selecting an optimal set of investment stocks is of a huge interest for both individual and institutional investors. This paper compares the hybrid multiple criteria decision making (MCDM) approach to selecting the best stock to invest in, with the stock selection using modern portfolio theory (MPT). When selecting stocks, it is very important to thoroughly analyse stocks, according to multiple criteria, including their equity market indicators, as well as financial indicators. The objective of the research is to compare the stock selection using a hybrid MCDM approach and MPT, which includes only the equity market indicators. The analysed sample includes 18 stocks, which are CROBEX components on the Croatian capital market from January 2017 to January 2019. The rankings of stocks were calculated using five MCDM methods. These were then used to obtain the final hybrid stock ranking, which was compared to the MPT stock selection. The results show that there is a significant difference in the stock rankings. However, the stocks which have not entered any portfolio in MPT selection were ranked as lowest according to the hybrid MCDM approach, which confirms that those stocks are the worst to invest in. The research can serve as a guidance for investors to use all available stock information in their decision making process of investment

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

Machine learning of radial basis function neural network based on Kalman filter: Implementation

U ovom radu su prikazani eksperimentalni rezultati primene tri nova sekvencijalna algoritma mašinskog učenja u cilju optimizacije parametara veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Uvedena su tri nova sekvencijalna algoritma mašinskog učenja: linearizovani Kalmanov filtar, linearizovani informacioni filtar, algoritam specifične aproksimacije momenata Gausove raspodele. Nakon prikaza odgovarajućih matematičkih modela datih u prvom delu ovog rada, u ovom delu razvijeni algoritmi su testirani u MATLAB® programskom okruženju razvojem odgovarajućeg softverskog koda i primenom test skupova podataka. Iako svi izabrani test skupovi podataka predstavljaju realne probleme, razvijeni algoritmi su testirani i na realnom inženjerskom problemu modeliranja izgleda segmenta obrađene površine. Sva tri algoritma su prilikom modeliranja ovih problema pokazala visok stepen tačnosti.In this paper we test three new sequential machine learning algorithms for radial basis function (RBF) neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network

PERFORMANSE PODUZEĆA U HRVATSKOM SEKTORU INFORMACIJSKO-KOMUNIKACIJSKE TEHNOLOGIJE (ICT)

Koristeći se podatcima Financijske agencije i Državnog zavoda za statitstiku u ovom se radu istražuju performanse cjelokupne populacije poduzeća u hrvatskom ICT sektoru. Prosječne performanse poduzeća u različitim industrijama upućuju na heterogenost industrija. Heterogenost je vidljiva u produktivnosti, profitabilnosti, opremljenosti kapitalom, minimalnoj efikasnoj veličini poduzeća. Usporedba performansi malih, srednjih i velikih poduzeća pokazuje da veličina poduzeća utječe na njihovu uspješnost. U ICT industrijama sve je veće značenje velikih poduzeća u tržišnim udjelima, ostvarenim profitima, zapošljavanju. Potencijalna uloga institucija u poboljšanju post-ulaznih performansa novih poduzeća u ICT sektoru je u razvoju tržišta kapitala i osiguravanju odgovarajuće obrazovne strukture zaposlenih kako bi se novim poduzećima u ovim mladim industrijama omogućili rast i preživljavanje

Machine learning of radial basis function neural network based on Kalman filter: Introduction

U ovom radu se analizira problem mašinskog učenja veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Prikazana su tri nova sekvencijalna algoritma mašinskog učenja: prvi algoritam direktno primenjuje linearizovani Kalmanov filtar kao algoritam mašinskog učenja, drugi algoritam primenjuje dual Kalmanovom filtru pod nazivom linearizovani informacioni filtar, dok treći algoritam na poseban način aproksimira prvi i drugi moment Gausove raspodele. U radu se naglašavaju osnovne prednosti koje pomenuti algoritmi imaju u poređenju sa konvencionalnim vidovima mašinskog učenja. Za sva tri algoritma razvijen je odgovarajući matematički model veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa. Analizirane su osnovne postavke izvedenih algoritama u cilju njihove primene na složene probleme u inženjerskoj praksi.This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice