Business Ecosystem & Data Ecosystem: Introduction to International Workshop on Big Data for Business Ecosystems

The possibilities of integrating business ecosystems and data ecosystems are considered. Their interaction is considered in the aspect of information exchange in open complex sociotechnical self-organizing systems. Modeling the interaction in such network structures makes it possible to determine the mechanisms of self-regulation that allow to ensure the sustainability of the ecosystem. Two models of network interaction are given, based on the analogy with information exchange in the models of production systems, presented as holon systems integrated with agents

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling

Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.National Science Foundation (IRI-9024877

Optimization of the structure of insulating composite materials

The article deals with the interdependent relationship between the properties of a structure and the properties of a material, which sets the task of reducing them, even they are indistinguishable, to a certain integrity. The object of research and analysis in the article is a building structure, which is seen as an open self-organized complex structural system. In the main part, the processes of the formation of structures are considered, as well as the classification of structural elements. The article concludes with structural changes related to the self-support and self-development of the network of active elements, which allow the manifestation of adaptation effects and the design-system to function during the normalized period. The importance of self-organization processes during the development and operation of construction systems allow us to attribute it to a self-organizing system. Thus, the building structure can be represented as an open and complex self-organizing system

The self organizing map of neighbour stars and its kinematical interpretation

The Self-Organizing Map (SOM) is a neural network algorithm that has the special property ofcreating spatially organized tepresetüatioes of various features of input signals. The resulting maps resemble realneural structures found in the cortices of developed animal brains.: Also, the SOM. has been successful in various pattern recognition tasks involving noisy signals, as for instance, speech recognition and for this reason we are studying its application to some astronomical problems. In this paper w~ present the 2-D mapping and subsequerít study of one local sample of 12000 stars using SOM. The available attributes are 14: 3-D position and velocitiesvphotometric indexes, spectral type and luminosity class. The possible location of halo, thick disk and thin disk stars is discussed. Their kinematical properties are also compared using the velocity distribution moments up to order four.Peer ReviewedPostprint (published version

Relevance of Dynamic Clustering to Biological Networks

Network of nonlinear dynamical elements often show clustering of synchronization by chaotic instability. Relevance of the clustering to ecological, immune, neural, and cellular networks is discussed, with the emphasis of partially ordered states with chaotic itinerancy. First, clustering with bit structures in a hypercubic lattice is studied. Spontaneous formation and destruction of relevant bits are found, which give self-organizing, and chaotic genetic algorithms. When spontaneous changes of effective couplings are introduced, chaotic itinerancy of clusterings is widely seen through a feedback mechanism, which supports dynamic stability allowing for complexity and diversity, known as homeochaos. Second, synaptic dynamics of couplings is studied in relation with neural dynamics. The clustering structure is formed with a balance between external inputs and internal dynamics. Last, an extension allowing for the growth of the number of elements is given, in connection with cell differentiation. Effective time sharing system of resources is formed in partially ordered states.Comment: submitted to Physica D, no figures include

A Decision Support System for Market Segmentation - A Neural Networks Approach

Market segmentation refers to the subdividing of a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix [Kotler 1980]. The reason for segmenting a market is that consumers are often numerous, geographically dispersed, and heterogeneous, and therefore seek varying benefits from the products they buy. Consumers within a segment are expected to have homogeneous buying preferences whereas those in different segments tend to behave differently. By properly identifying the benefit segment of a firm\u27s product, the marketing manager can target the sales effort at specific groups of consumers rather than at the total population. The identification of consumer segments is of critical importance for key strategic issues in marketing involving the assessment of a firm\u27s opportunities and threats. The marketing manager must evaluate the potential of the firm\u27s products in the target segment and ultimately select the most promising strategy for the segment. In thisresearch, we introduce a new approach, a neural networks based method, to discover market segments and configure them into meaningful structures. The particular type of neural networks, the Self-Organizing Map networks, can be used as a decision support tool for supporting strategic decisions involving identifying and targeting market segments. The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to market segmentation. A computer program for implementing the SOM neural networks is developed and the results will be compared with other clustering approaches. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for market segmentation