Components of Program for Analysis of Spectra and Their Testing

The spectral analysis of aqueous solutions of multi-component mixtures is used for identification and distinguishing of individual componentsin the mixture and subsequent determination of protonation constants and absorptivities of differently protonated particles in the solution in steadystate (Meloun and Havel 1985), (Leggett 1985). Apart from that also determined are the distribution diagrams, i.e. concentration proportions ofthe individual components at different pH values. The spectra are measured with various concentrations of the basic components (one or severalpolyvalent weak acids or bases) and various pH values within the chosen range of wavelengths. The obtained absorbance response area has to beanalyzed by non-linear regression using specialized algorithms. These algorithms have to meet certain requirements concerning the possibility ofcalculations and the level of outputs. A typical example is the SQUAD(84) program, which was gradually modified and extended, see, e.g., (Melounet al. 1986), (Meloun et al. 2012)

Development and Introduction of Methods, Algorithms and Programs of Digital Modelling, Identification and Optimal Control of Complex Systems (Example of Continuous and Periodic Processes of Chemical Technology)

Available from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Control of Hammerstein nonlinear system

The paper deals with the control of a nonlinear plant with the quadratic steady-state characteristics. The plant is described by Hemmarstein model and controlled by a deadbeat controller. The controller is designed by the classical complex area method. The choice of a sampling period T in the dependence of the plant character (with minimum or nonminimum phase) and of the model plant parameters is presented. The choice of T is based on an admitted actuator range and on the keeping the deadbeat – strong version criterion condition. Simulation examples are presented

Identification of nonlinear systems based on mathematical physical analysis and least square method

The paper deals with the identification of nonlinear systems described by the nonlinear difference equations of the certain type. These equations are known in literature dealing with the systems modelling by means of neural network as the models of the second type. In some cases, when these models are nonlinear also in parameters and we want to use the least square method for parameter estimation, it is necessary to transform the models into linear forms. Our attention has chiefly been paid to the practical utilization of the proposed algorithm and to this verification on simulated example

ARTIFICIAL NEURAL NETWORKS IN PROCESS CONTROL

Cílem tohoto příspěvku je demonstrovat možnosti uplatnění jednoho z typů neuronových sítí na konkrétním příkladu řízení kontinuálního bioreaktoru. V analogii s postupy klasických metod regulace bude nejprve popsána tvorba dynamického neuronového modelu a tento model bude následně použit při návrhu řízení bioreaktoru metodou regulace s vnitřním modelem modifikovanou tak, aby mohla využít umělou neuronovou síť jak pro tvorbu modelu soustavy, tak pro regulátor. I když veškerá demonstrace postupů použitých v tomto příspěvku bude prováděna na modelu kontinuálního bioreaktoru, je zřejmé, že uvedená metodika může najit využití v celé řadě jiných oblastí.There is demonstrated one possibility of artificial neural networks usage in process control, in this paper. First, it is shown how to create dynamic neural model of a plant, then its inverse neural model and in the end, these models are used to synthesise the Internal Model Control network. Although this procedure is demonstrated on the example of continual bioreactor control, it can be applied to many other spheres

USING OF ARTIFICIAL NEURAL NETWORK FOR THE IDENTIFICATION OF DYNAMIC PROPERTIES OF HYDRAULIC-PNEUMATIC SYSTEM

Cílem uvedeného příspěvku je demonstrovat použití umělých neuronových sítí k řešení praktických úloh identifikace dynamického chování složitých nelineárních soustav. Byl zkoumán matematicko-fyzikální model hydraulicko-pneumatické soustavy za účelem vytvoření alternativy tohoto modelu, a to ve tvaru umělé neuronové sítě (UNS). Model představuje obecně nelineární vícerozměrnou soustavu se dvěma vstupy a dvěma výstupy. Přičemž vstupními veličinami jsou průtoky čerpadly a výstupními veličinami jsou výšky hladin v dolních nádržích soustavy. Vstupy i výstupy soustavy jsou reprezentovány unifikovanými napěťovými signály. Řešení úlohy spočívalo v popisu jednotlivých závislostí mezi konkrétními vstupními a výstupními veličinami pomocí UNS. K řešení úlohy byl použit Neural Network Toolbox výpočetního systému MATLAB/SIMULNK.The aim of the paper is to demonstrate using of artificial neural networks for the solution of practical problems of the identification of the complex non-linear systems' dynamic behavior. The mathematical model of the hydraulic-pneumatic system was investigated in order to build an alternative of this model, namely in the form of the artificial neural network (ANN). The model presents generally nonlinear multi-dimensional system with two inputs and two outputs. Input variables are flows through controlled pumps and output variables are water levels in the bottom tanks of the system. Both inputs and outputs of the system are represented as unified voltage signals. Solution of the problem consisted in the description of selected single dependences between particular input and output variables by means of ANN. For the problem solution Neural Network Toolbox was used a toolbox of the computing system MATLAB/SIMULINK

POSSIBILITY OF SMART CAR SPEED CONTROL USING SOFT COMPUTING

Smart speed control of a car is the focus of the paper. There is introduced the possibility how to automatically control the car speed smoothly in the daily traffic. The technique uses nonlinear neural car model together with differential evolution search technique to determine continuously convenient throttle power so that the car speed is optimal with respect to defined cost function – the cost function can consider actual speed limits as well as speed smoothness