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Wyszukujesz frazę "Duch, W." wg kryterium: Autor


Wyświetlanie 1-4 z 4
Tytuł:
A survey of factors influencing MLP error surface
Autorzy:
Kordos, M.
Duch, W.
Powiązania:
https://bibliotekanauki.pl/articles/970445.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
sieć neuronowa
powierzchnia błędów
wizualizacja
trajektoria uczenia się
neural networks
MLP
error surface
visualization
learning trajectory
Opis:
Visualization of neural network error surfaces and learning trajectories helps to understand the influence of numerous factors on the neural learning process. This understanding can be used to improve training and design of MLP networks. The following topics are discussed using a few benchmark datasets for illustration: general error surface properties including local minima, plateaus and narrow funnels, their dependence on network structure, input data, transfer and error functions, consequences of weight initialization, and interesting directions in the weight space. The error surfaces are shown in 3-dimensional PCA-based projections. Finally a possibility of effective weight number reduction is discussed.
Źródło:
Control and Cybernetics; 2004, 33, 4; 611-631
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification, Association and Pattern Completion Using Neural Similarity Based Methods
Autorzy:
Duch, W.
Adamczak, R.
Diercksen, G. H. F.
Powiązania:
https://bibliotekanauki.pl/articles/911147.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sieć neuronowa
klasyfikacja
rozpoznawanie obrazów
neural networks
classification
association
pattern recognition
Opis:
A framework for Similarity-Based Methods (SBMs) includes many classification models as special cases: neural networks of the Radial Basis Function type, Feature Space Mapping neurofuzzy networks based on separable transfer functions, Learning Vector Quantization, variants of the k nearest neighbor methods and several new models that may be presented in a network form. Multilayer Perceptrons (MLPs) use scalar products to compute a weighted activation of neurons, combining soft hyperplanes to provide decision borders. Distance-based multilayer perceptrons (D-MLPs) evaluate the similarity of inputs to weights offering a natural generalization of standard MLPs. A cluster- based initialization procedure determining the architecture and values of all adaptive parameters is described. Networks implementing SBM methods are useful not only for classification and approximation, but also as associative memories, in problems requiring pattern completion, offering an efficient way to deal with missing values. Non-Euclidean distance functions may also be introduced by normalization of the input vectors in an extended feature space. Both the approaches dramatically influence the shapes of decision borders. An illustrative example showing these changes is provided.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 747-766
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simple cyclic movements as a distinct autism feature - computational approach
Autorzy:
Dobosz, K.
Mikołajewski, D.
Wójcik, G. M.
Duch, W.
Powiązania:
https://bibliotekanauki.pl/articles/305787.pdf
Data publikacji:
2013
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
computational neuroscience
neural networks
attractor networks
motor control
repetitive movements
ion channels
Autism Spectrum Disorders
ASD
emergent simulator
GENESIS simulator
Opis:
A diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders (ASD). Each year, the percentage of children diagnosed with ASD is growing. One common diagnostic feature in individuals with ASD is the tendency to exhibit atypical simple cyclic movements.The motor brain activity seems to generate a periodic attractor state that is hard to escape. Despite numerous studies, scientists and clinicians do not know exactly if ASD is a result of a simple yet general mechanism or of a complex set of mechanisms (either on the neural, molecular and system levels). Simulations using the biologically - relevant neural network model presented here may help to reveal the simplest mechanisms that may be responsible for specific behavior. Abnormal neural fatigue mechanisms may be responsible for motor symptoms as well as many (or perhaps all) of the other symptoms observed in ASD.
Źródło:
Computer Science; 2013, 14 (3); 475-489
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational approach to understanding Autism Spectrum Disorders
Autorzy:
Duch, W.
Nowak, W.
Meller, J.
Osiński, G.
Dobosz, K.
Mikołajewski, D.
Wójcik, G. M.
Powiązania:
https://bibliotekanauki.pl/articles/305295.pdf
Data publikacji:
2012
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
computational neuroscience
neural networks
autism
Autism Spectrum Disorders
ASD
Opis:
Every year the prevalence of Autism Spectrum of Disorders (ASD) is rising. Is there a unifying mechanism of various ASD cases at the genetic, molecular, cellular or systems level? The hypothesis advanced in this paper is focused on neural dysfunctions that lead to problems with attention in autistic people. Simulations of attractor neural networks performing cognitive functions help to assess system long-term neurodynamics. The Fuzzy Symbolic Dynamics (FSD) technique is used for the visualization of attractors in the semantic layer of the neural model of reading. Large-scale simulations of brain structures characterized by a high order of complexity requires enormous computational power, especially if biologically motivated neuron models are used to investigate the influence of cellular structure dysfunctions on the network dynamics. Such simulations have to be implemented on computer clusters in a grid-based architectures.
Źródło:
Computer Science; 2012, 13 (2); 47-61
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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