scholarly journals Advances in ecological modelling of soil properties by self-organizing feature maps of natural environment of Lower Silesia (Poland)

2011 ◽  
Vol 78 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Andrzej Stankiewicz ◽  
Piotr Kosiba
Keyword(s):  
Soil Properties ◽  
Natural Environment ◽  
Environmental Changes ◽  
Parent Rock ◽  
Complex Data ◽  
Ecosystem Structure ◽  
Feature Maps ◽  
Stage Of Development ◽  
Lower Silesia ◽  
Self Organizing

The paper provides the use of self-organizing feature maps for determination of soil properties in its initial stage of development formed of massive rocks and how SOFM can be used for the study of environmental objects. The study area was Lower Silesia (Poland) overgrown with common, unique and protected vegetation of lichens, bryophytes and vascular plants. The parent rock of the studied soils consists of Miocene volcanites from the middle part of the Sudety Margin Fault. Soil samples were collected from 20 sites. The soil reaction (pH) and concentrations of Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, S, Ti, Zn in surface soils were analyzed. Statistical analysis was carried out by one-way ANOVA. The SOFM was used to demonstrate the non-linear ordination and visualization of soil properties. The SOFM showed the influence of parent rock on soil chemical properties generated by it. SOFM appeared to be effective and proper/fit for phenomena and processes taking place in natural environment and is useful in ecology and ought to be taken into account as a possible tool of estimation of various plants and their biotopes. The model can be useful as alternative techniques in modelling the ecological complex data, and provide a novel framework for the discovery and forecasting of ecosystem structure and behaviours in response to environmental changes.

Modeling student knowledge with self-organizing feature maps.

1993 ◽  
Author(s):  
Steven A. Harp ◽  
Tariq Samad ◽  
Michael Villano
Keyword(s):  
Feature Maps ◽  
Student Knowledge ◽  
Self Organizing

Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents

2021 ◽  
Vol 22 (4) ◽  
pp. 171-180
Author(s):  
V. B. Melekhin ◽  
M. V. Khachumov
Keyword(s):  
Intelligent Agents ◽  
Environmental Changes ◽  
A Priori ◽  
Operating Conditions ◽  
Basic Principles ◽  
Purposeful Behavior ◽  
Problem Solvers ◽  
Boundary Estimates ◽  
Self Learning ◽  
Self Organizing

We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.

scholarly journals Classification of chosen orchard pests using the self-organizing feature maps neural network

2012 ◽  
Vol 7 (47) ◽  
pp. 6357-6362 ◽  
Author(s):  
Pilarski Krzysztof ◽  
Boniecki Piotr ◽  
Slosarz Piotr ◽  
Dach Jacek ◽  
Boniecka Piekarska Hanna ◽  
...  
Keyword(s):  
Neural Network ◽  
The Self ◽  
Feature Maps ◽  
Orchard Pests ◽  
Self Organizing

scholarly journals Methods of Landscape Valorization and Possibilities of Its Application in Hunting Area Categorisation

2020 ◽  
Author(s):  
Szewczyk Grzegorz ◽  
Krzysztof Lipka ◽  
Piotr Wężyk ◽  
Karolina Zięba-Kulawik ◽  
Monika Winczek
Keyword(s):  
Natural Environment ◽  
Functional Role ◽  
Large Scale ◽  
Forest Cover ◽  
Agricultural Landscape ◽  
Environmental Changes ◽  
Environmental Values ◽  
Small Game ◽  
Forest Habitats

As a result of environmental changes, assessment indexes for the agricultural landscape have been changing dramatically. Being at the interface of human activity and the natural environment, hunting is particularly sensitive to environmental changes, such as increasing deforestation or large-scale farming. The classical categorisation of hunting grounds takes into account the area, forest cover, number of forest complexes, fertility of forest habitats, lack of continuity of areas potentially favourable to wild animals. Landscape assessment methods used in architecture often better reflect the actual breeding and hunting value of a given area, especially in relation to fields and forests. The forest-field mosaic, large spatial fragmentation as well as interweaving of natural environment elements with buildings do not have to be the factors that limit the numbers of small game. Identification of the constituents of architectural-landscape interiors: content and significance assessment, determination of the functional role or assessment based on the general environmental values being represented take into account factors important for the existence of game, in particular small game.

scholarly journals SELF-ORGANIZING MAPS CLASSIFICATION OF SOIL PROPERTIES IN LEVEE USING DIFFERENT GEOPHYSICAL DATA

2010 ◽  
Vol 66 (1) ◽  
pp. 89-99
Author(s):  
Ayumu MIYAKAWA ◽  
Takeshi TSUJI ◽  
Toshifumi MATSUOKA ◽  
Tsuyoshi YAMAMOTO
Keyword(s):  
Soil Properties ◽  
Geophysical Data ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps

2002 ◽  
Vol 24 (4-5) ◽  
pp. 167-179 ◽  
Author(s):  
Torsten Mattfeldt ◽  
Hubertus Wolter ◽  
Danilo Trijic ◽  
Hans‐Werner Gottfried ◽  
Hans A. Kestler
Keyword(s):  
Cluster Analysis ◽  
Comparative Genomic Hybridization ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Data Matrix ◽  
Comparative Genomic ◽  
Feature Maps ◽  
Genomic Hybridization ◽  
A Genome ◽  
Self Organizing

Comparative genomic hybridization (CGH) is an established genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated.

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