Browsing by Subject "backpropagation"

Now showing 1 - 5 of 5

Access status: Open Access ,
A neural network approach to recognition of the selected human motion pattern
(Wydawnictwa AGH, 2011) Mikrut, Zbigniew; Smoleń, Magdalena
W artykule zaproponowano metodę reprezentacji czynności wykonywanej przez człowieka w postaci histogramu kierunków pola ruchu. Histogram był obliczany w masce konturu sylwetki i agregowany do ośmiu kierunków. Zbiór danych powstał na podstawie analizy filmu, na którym cztery osoby wykonywały 9 czynności, takich jak chodzenie, siadanie i wstawanie z krzesła czy też sięganie (w górę i do przodu). Do rozpoznawania wykorzystano sieć neuronową typu backpropagation z jedną warstwą ukrytą. Osiągnięto wyniki rozpoznawania na poziomie 80-88% dla pojedynczych osób. Stwierdzono, że nie jest możliwe rozpoznawanie czynności danej osoby za pomocą sieci nauczonej danymi innej osoby.
Access status: Open Access ,
Applying neural network in computing filling coefficient of four-stroke internal combustion engine
(2011) Bera, Piotr
Neural networks consist of many simple elements operating in parallel. In supervised training they are capable of finding their own solution to a particular problem, given only examples of proper behavior. It is a very useful method of solving complex, non-linear problems. The following article discusses the usage of artificial neural network to compute the value of filling coefficient of four-stroke internal combustion engines as the function of crankshaft rotational speed and throttle opening angle. The paper presents the idea of a static, two-layer feedforward network trained with the basic backpropagation algorithm in which the weights and biases are updated in the direction of the negative gradient. The article discusses network architecture and data structure, training parameters and result analysis.
Access status: Open Access ,
Assessment of approaches for the extraction of building footprints from pléiades images
(Wydawnictwa AGH, 2021) Taha, Lamyaa Gamal El-deen; Ibrahim, Rania Elsayed
The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
Access status: Open Access ,
Metoda wymuszania wewnętrznych wzorców w jednokierunkowej sieci klasyfikującej
(Wydawnictwa AGH, 2006) Kolibabka, Marcin; Cader, Andrzej
Creating and later learning of one-way neural networks depends from many factors. Selection of many them has estimated and experimental character. The proposed in the article method allows to the weakness of the influence of the not optimal choice of the net structure, also speed and momentum values are less influential then in classic Back Propagation Method.
Access status: Open Access ,
Strategie poprawy efektywności uczenia sieci neuronowej
(Wydawnictwa AGH, 2007) Orzechowski, Patryk; Mikrut, Zbigniew
This article presents the results of experiments carried out before and during the learning process of artificial neural network (backpropagation), used for handwritten digits recognition. Some unconventional techniques are described, such as an algorithm of slant correction and two variants of sequential learning, basing on the recognition reliability of the specific digit and statistical confusion matrix.