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Computational modelling of thermal behavior of DC motor
Grepl, Robert ; Wierciak, J. ; Vlach, R.
The paper deals with computational modelling of thermal behavior ofdrive system with DC motor. Motivation to built dynamical thermal model is toincrease utility value performance of electrical motor in particular roboticapplication. There is mathematical model presented in the paper, describedidentification of parameters process and shown results for two kind ofidentification approaches. Usage of neural networks approximators ismentioned.
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Floods Prediction Possibility by Means of Neural Networks
Neruda, M. ; Neruda, Roman
The article deals with studying rainfall-runoff relations on the Plouťnice catchment. With help of a method Artificial neural network we established few models of rainfall-runoff dependence in past days. The neural model then can predict runoff values onthe base of historical data. Calculated runoff can help with prediction of flooding on rivers.
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O nemožnosti učení neuronů s impulsy
Sgall, Jiří ; Šíma, J.
The computational complexity of training a single spiking neuron N with adjustable synaptic delays and binary coded inputs and output is studied. A synchronization technique is introduced so that the results concerning the non-learnability of spiking neurons with binary delays are generalized to arbitrary delays.
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Use of Neural Networks in Equity Trading
Lahodová, Martina ; Veselá, Jitka (advisor) ; Stádník, Bohumil (referee)
The neural networks have been the fastest developing area of computer science lately. They have strong interdisciplinary character, so that they can be applied in many fields of human activity e.g. capital markets. The objective of the thesis is to apply a perceptron model to predicting future value of a sample of shares, to set the accuracy of prediction and bring the conclusion of reliable use of the neural networks. Opening chapters are concerned with general principles of neural networks functioning, their classification and different ways of their "learning". Analytic chapter is based on the creation and use of the perceptron model and the analysis of given results.
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Applications of neural networks and Elliot´s waves on selected shares
Polaková, Soňa ; Veselá, Jitka (advisor) ; Musílek, Petr (referee)
Using modern methods of share quotations forecasting is the main goal of this thesis. The special accent is placed on forecasting the trend by means of artificial neural network especially on the optimalization of variables in the training process. Elliot's wave theory is applied in the second part of the thesis, particularly on prediction of future share quotation progress. Buying or selling signal generated by these two methods is consequently compared with ex-post signal yielding a profit. Lastly, successfulness of using these methods for forecasting at stock market is evaluated.
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