Upravljanje dinamičkim sistemima primenom adaptivnih ortogonalnih neuronskih mreža

The goal of the research in the PhD dissertation is control of dynamical systems by using new types of orthogonal endocrine neural networks, in order to improve their performances. Standard artificial neural networks are described, as well as their historical development and basic types of learning algorithms. Further, possibilities for neural networks applicability within control logic of dynamical systems are presented, as well as the current state of the art of orthogonal and endocrine neural networks. Performance improvement of the laboratory model of a servo system by using a standard neural network with the backpropagation type of learning is analyzed. In addition, a method for selection and optimization of training data, as an efficient way of information preprocessing for the purpose of improving performances of a neural network, is presented. A detailed description of orthogonal functions and implementation methods of endocrine factors inside standard neural networks are provided. By implementation of orthogonal activation functions of neurons, verification of their applicability in control of dynamical systems was performed. The laboratory model of the magnetic levitation system was used to test the designed orthogonal neural network. Furthermore, the endocrine orthogonal neural network based on the biological processes of excitation and inhibition is designed. Network performance checkup is performed by testing its predictive abilities when working with time series data. Final dissertation researches refer to development of hybrid systems. The implemented adaptive endocrine neuro-fuzzy hybrid system is tested through modeling of a laboratory servo system. Other hybrid structure, based on a combination of an orthogonal endocrine neural network and an orthogonal endocrine neuro-fuzzy hybrid system, is designed with the aim to form symbiosis of the positive characteristics of the individual networks. Verification of this structure was performed by using it for PID controller parameters adjustments

Synthesis and characterization of mixed oxides derivate from Li modified Mg-Al hydrotalcites

Lithium modified Mg/Al hydrotalcite-like samples with different Li content were synthesized using co-precipitation followed by calcination at 500 °C. The samples were characterized by means of XRD, DRIFTS, SEM-EDS, LDPSA and MIP. Results from this study indicated that the addition of Li modifier influences the change in structural, textural and morphological characteristics, more pronounced in samples with higher lithium content

Establishment and in-house validation of stem-loop rt pcr method for microrna398 expression analysis

MicroRNAs (miRNAs) belong to the class of small non-coding RNAs which have important roles throughout development as well as in plant response to diverse environmental stresses. Some of plant miRNAs are essential for regulation and maintenance of nutritive homeostasis when nutrients are in excess or shortage comparing to optimal concentration for certain plant species. Better understanding of miRNAs functions implies development of efficient technology for profiling their gene expression. We set out to establish validate the methodology for miRNA gene expression analysis in cucumber grown under suboptimal mineral nutrient regimes, including iron deficiency. Reverse transcription by "stem-loop" primers in combination with Real time PCR method is one of potential approaches for quantification of miRNA gene expression. In this paper we presented a method for "stem loop" primer design specific for miR398, as well as reaction optimization and determination of Real time PCR efficiency. Proving the accuracy of this method was imperative as "stem loop" RT which consider separate transcription of target and endogenous control. The method was verified by comparison of the obtained data with results of miR398 expression achieved using a commercial kit based on simultaneous conversion of all RNAs in cDNAs

Synthesis and characterization of mixed oxides derivate from Li modified Mg-Al hydrotalcites

Lithium modified Mg/Al hydrotalcite-like samples with different Li content were synthesized using co-precipitation followed by calcination at 500 °C. The samples were characterized by means of XRD, DRIFTS, SEM-EDS, LDPSA and MIP. Results from this study indicated that the addition of Li modifier influences the change in structural, textural and morphological characteristics, more pronounced in samples with higher lithium content

FUZZY LOGIC-BASED CONTROL OF THREE-DIMENSIONAL CRANE SYSTEM

The control of three-dimensional (3D) crane system represents one of the most widely challenging control problems. 3D crane system is used for lifting and moving loads horizontally, as well as lowering and realizing the gripper to the original position. In this paper fuzzy logic-based control of three-dimensional crane (3D) system is presented. Hence the system produces oscillations during moving loads, the main objective of the designed controller is to control the swing angle. As a plant for controller design, the bond graph model of 3D crane system is used. To verify the effectiveness of the proposed control method, several digital simulations with concrete values of parameters are performed using Matlab. The simulations results show that the proposed fuzzy logic control produce better performance in regard to the reduction of undesired oscillations.Key words: bond graph, 3D crane, Dymola, fuzzy control, modeling and simulation, Matlab/Simulin

LABORATORY CNC MACHINE FOR EDUCATION OF STUDENTS IN CONTROL SYSTEMS ENGINEERING

Modern states seek to build a society based on knowledge, and in this sense, the IPA project ADRIA HUB aims to connect students, universities and companies into a single entity in which each party realizes many benefits. Practical part of this project consists of the pilot projects related to the improvements in the woodworking industry. For the purpose of the project realization, a specific laboratory CNC machine was designed and implemented by the Laboratory for Modeling, Simulation and Control Systems at the Faculty of Electronic Engineering in Niš. CNC machine, presented in this paper, is now actively used in laboratory work. Students have the opportunity to gain practical knowledge and master the techniques of controlling this machine as part of their studies in Control Systems Engineering

DATA DENOISING PROCEDURE FOR NEURAL NETWORK PERFORMANCE IMPROVEMENT

This paper will present training data denoising procedure for neural network performance improvement. Performance improvement will be measured by evaluation criterion which is based on a training estimation error and signal strength factor. Strength factor will be obtained by applying denoising method on a default training signal. The method is based on a noise removal procedure performed on the original signal in a manner which is defined by the proposed algorithm. Ten different processed signals are obtained from the performed method on a default noisy signal. Those signals are then used as a training data for the nonlinear autoregressive neural network learning phase. Empirical comparisons are made at the end, and they show that the proposed denoising procedure is an effective way to improve network performances when the training set possesses the significant noise component

Upravljanje dinamičkim sistemima primenom adaptivnih ortogonalnih neuronskih mreža

The goal of the research in the PhD dissertation is control of dynamical systems by using new types of orthogonal endocrine neural networks, in order to improve their performances. Standard artificial neural networks are described, as well as their historical development and basic types of learning algorithms. Further, possibilities for neural networks applicability within control logic of dynamical systems are presented, as well as the current state of the art of orthogonal and endocrine neural networks. Performance improvement of the laboratory model of a servo system by using a standard neural network with the backpropagation type of learning is analyzed. In addition, a method for selection and optimization of training data, as an efficient way of information preprocessing for the purpose of improving performances of a neural network, is presented. A detailed description of orthogonal functions and implementation methods of endocrine factors inside standard neural networks are provided. By implementation of orthogonal activation functions of neurons, verification of their applicability in control of dynamical systems was performed. The laboratory model of the magnetic levitation system was used to test the designed orthogonal neural network. Furthermore, the endocrine orthogonal neural network based on the biological processes of excitation and inhibition is designed. Network performance checkup is performed by testing its predictive abilities when working with time series data. Final dissertation researches refer to development of hybrid systems. The implemented adaptive endocrine neuro-fuzzy hybrid system is tested through modeling of a laboratory servo system. Other hybrid structure, based on a combination of an orthogonal endocrine neural network and an orthogonal endocrine neuro-fuzzy hybrid system, is designed with the aim to form symbiosis of the positive characteristics of the individual networks. Verification of this structure was performed by using it for PID controller parameters adjustments

Input data preprocessing method for exchange rate forecasting via neural network

The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006

Establishment and in-house validation of stem-loop RT PCR method for MicroRNA398 expression analysis

MicroRNAs (miRNAs) belong to the class of small non-coding RNAs which have important roles throughout development as well as in plant response to diverse environmental stresses. Some of plant miRNAs are essential for regulation and maintenance of nutritive homeostasis when nutrients are in excess or shortage comparing to optimal concentration for certain plant species. Better understanding of miRNAs functions implies development of efficient technology for profiling their gene expression. We set out to establish validate the methodology for miRNA gene expression analysis in cucumber grown under suboptimal mineral nutrient regimes, including iron deficiency. Reverse transcription by “stem-loop” primers in combination with Real time PCR method is one of potential approaches for quantification of miRNA gene expression. In this paper we presented a method for “stem loop” primer design specific for miR398, as well as reaction optimization and determination of Real time PCR efficiency. Proving the accuracy of this method was imperative as “stem loop” RT which consider separate transcription of target and endogenous control. The method was verified by comparison of the obtained data with results of miR398 expression achieved using a commercial kit based on simultaneous conversion of all RNAs in cDNAs. [Projekat Ministarstva nauke Republike Srbije, br. 173005 i br. ON-173028