Acta Technica Corviniensis

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ACTA TECHNICA CORVINIENSIS – BULLETIN of ENGINEERINGPunct2D type and the object of Hiperbola2Dtype.Further, the execution’s control is transmitted tothe object of Vector type, in orderto add the normal previously created in the listof 2D elements of the geometric construction,and then will be destroyed the instance of theDreapta2D class. Finally, will be redrawn thegeometric construction, which will include nowalso the normal to the previously createdhyperbola by using the object of Graphics2Dtype.CONCLUSIONFigure 2. Sequence diagram for drawing a hyperbolaThe diagrams were achieved by an approach ina new manner, multidisciplinary, of theinformatics application, including both themodern pedagogy methods, and thecomponents specific to the discipline to bestudied.Thus, was achieved the connection between thedidactic actions and the purposes and objectivesscientifically established, by elaborating of newmethods and assimilating of new means, capableto increase the school efficiency, allowing thepupils and students to acquire the systemrequired by knowledge’s and their applicationtechniques in conditions as optimal possible.REFERENCES[1.] Booch G., Rumbaugh J., Jacobson I., TheUnified Modeling Language User Guide,Addison Wesley, 1999[2.] Fowler M., Scott K., UML Distilled: A Brief Guideto the Standard Object Modeling Language,Addison Wesley, Readings MA, USA, 2000[3.] Odell J., Advanced Object Oriented Analysis &Design using UML, Cambrige University Press,1998[4.] Oestereich B., Developing Software with UML,Addison Wesley, 1999[5.] Rumbaugh J, Jacobson I., Booch G., The UnifiedModeling Language Reference Manual,Addison Wesley, 1999Figure 3. Sequence diagram for drawing a thenormal to a hyperbolaGiving back the control to the object of Desen2Dtype, further will be instantiated the object ofDreapta2D type, representing the normal tohyperbola, then will be destroyed the object ofAUTHORS & AFFILIATION1.ANCA IORDAN, 2. MANUELA PĂNOIU1. 2. 3FACULTY OF ENGINEERING HUNEDOARA,UNIVERSITY “POLITEHNICA” TIMISOARA, ROMANIA522008/ACTA TECHNICA CORVINIENSIS/Tome I
1GELU OVIDIU TIRIAN, 1 STELA RUSU-ANGHEL,2CAMELIA PINCA-BRETOTEANPREDICTION OF THE SIGNALS USINGTHE NEURONAL NETWORKSAbstract:This paper work refers to the prediction problems which are used with the help of the neuronalnetworks. The network is made of a neuron whose function is linear and who has the past few 5 inputvalues of the useful signal x(t) – this signal must be predicted.The training algorithm is Widrow-Hoff. This algorithm decreases the number of square errors betweenthe output of the network and the required value, and it eventually establishes the “weight” factor.Keywords:Prediction, algorithm, neuronal networks, signalINTRODUCTIONThis identification [2], [3] is based on someneuronal methods and it has been lately used aresearch method due to mathematical researchabout the approximation properties of theneuronal networks - MLP (MultilayerPerceptron) and RBF (Radial-Basis Function)type [1]. The development of this new domainhas been spectacular due to a remarkablecontribution of some Automatics scientists. Theyhave used the MLP networks for the non-linearidentification and they have decided to use theRBF-type patterns. Thus, we have to point outthe fact that the most important automaticapplication of those techniques, who are specificfor the neuronal networks, is the use of the MLPfeatures with sinoid nodes inside the hiddenlayer(s). On the other hand, those companieswho produce technical-scientific software havebegun developing specific facilities for theneuronal networks, so that the first version ofthe Neuronal Network Toolbox (NNT) should beenclosed in the MATLAB 4.2 environment – ithad a major impact on the interest of theautomatics specialists. NNT, from the earliest tothe most recent version, has been designed tocover a range of applications of the neuronalnetworks, the designers had no intention todevelop the Simulink blocks simultaneously [4].These blocks are used for designing dynamicpatterns.NEURONAL NETWORKS. TRAINING NEURONALNETWORKSThe neuronal networks can be made of simpleprocessing elements, such as perceptrons orneuronals (Figure no. 1), and they make up onelayernetworks, or of multiple elements and theymake up multi-layer networks [1]. All thesenetwork-types enclose some elements that aredistributed within the connection „weights"amongst different layers that make up eachnetwork [6]. The properties of the neuronalnetworks are: contain memory, shape© copyright FACULTY of ENGINEERING – HUNEDOARA, ROMANIA 53
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