TY  - CHAP
A1  - Grum, Marcus
ED  - Rutkowski, Leszek
ED  - Scherer, Rafał
ED  - Korytkowski, Marcin
ED  - Pedrycz, Witold
ED  - Tadeusiewicz, Ryszard
ED  - Zurada, Jacek M.
T1  - Learning representations by crystallized back-propagating errors
T2  - Artificial intelligence and soft computing
N2  - With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.
KW  - artificial neural networks
KW  - backpropagation
KW  - knowledge crystallization
KW  - second-order conditioning
KW  - cognitive levels
KW  - NMDL
Y1  - 2023
SN  - 978-3-031-42504-2
SN  - 978-3-031-42505-9
U6  - https://doi.org/10.1007/978-3-031-42505-9_8
SP  - 78
EP  - 100
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Mey, Jürgen
A1  - Scherler, Dirk
A1  - Zeilinger, Gerold
A1  - Strecker, Manfred
T1  - Estimating the fill thickness and bedrock topography in intermontane valleys using artificial neural networks
JF  - Journal of geophysical research : Earth surface
N2  - Thick sedimentary fills in intermontane valleys are common in formerly glaciated mountain ranges but difficult to quantify. Yet knowledge of the fill thickness distribution could help to estimate sediment budgets of mountain belts and to decipher the role of stored material in modulating sediment flux from the orogen to the foreland. Here we present a new approach to estimate valley fill thickness and bedrock topography based on the geometric properties of a landscape using artificial neural networks. We test the potential of this approach following a four-tiered procedure. First, experiments with synthetic, idealized landscapes show that increasing variability in surface slopes requires successively more complex network configurations. Second, in experiments with artificially filled natural landscapes, we find that fill volumes can be estimated with an error below 20%. Third, in natural examples with valley fill surfaces that have steeply inclined slopes, such as the Unteraar and the Rhone Glaciers in the Swiss Alps, for example, the average deviation of cross-sectional area between the measured and the modeled valley fill is 26% and 27%, respectively. Finally, application of the method to the Rhone Valley, an overdeepened glacial valley in the Swiss Alps, yields a total estimated sediment volume of 9711km(3) and an average deviation of cross-sectional area between measurements and model estimates of 21.5%. Our new method allows for rapid assessment of sediment volumes in intermontane valleys while eliminating most of the subjectivity that is typically inherent in other methods where bedrock reconstructions are based on digital elevation models.
KW  - sediment storage
KW  - sediment thickness
KW  - intermontane valleys
KW  - geomorphometry
KW  - artificial neural networks
Y1  - 2015
U6  - https://doi.org/10.1002/2014JF003270
SN  - 2169-9003
SN  - 2169-9011
VL  - 120
IS  - 7
SP  - 1301
EP  - 1320
PB  - American Geophysical Union
CY  - Washington
ER  -