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The paper presents the results of the research of the clustering algorithm DBSCAN practical implementation within the framework of the objective clustering inductive technology. As experimental, the data Aggregation and Compound of the Computing school of the East Finland University and the gene expression sequences of lung cancer patients of the database ArrayExpres were used. The architecture of...
This paper studies a statistical dataset describing submissions to the municipalities in Czech Republic. The dataset contains five submission-specific subgroups as interdependent time series. The research purpose is to build a suitable model for description of the process. In this work, the autoregressive and vector autoregressive models are used for fitting the data. The obtained results proved to...
The paper considers a classification problem of objects that are given by a set of measurements. An optimal model structure of classified objects is suggested to obtain for each class to solve the problem. To construct the structures, a new version of GMDH algorithm was developed having a new combined external criterion that includes accuracy and division ability of objects structure of each class...
The paper presents a new mechanism to apply evolutionary complication of models in the previously introduced hybrid COMBI-GA sorting-out algorithm to find optimal model structure. The mechanism is based on generation of model structures using binomial random number generator with low probability and specific mutation operator. The presented experimental results demonstrate that this algorithm performs...
The results of computational investigation of the generalized iterative algorithm GIA GMDH with active neurons are presented. The algorithm's architecture is based on hybridization of iterative and combinatorial search schemes and comprises six standard variants of typical GMDH algorithms. The experiments demonstrate high performance and accuracy of the algorithm. Results of using the GIA GMDH are...
The paper deals with the problem of stability during the solving of pattern recognition tasks from the point of view of transformation groups. It shows the possibility to avoid the necessity of regularization by using the geometric equaffine Lorentz transformation, exploiting as example the alpha-procedure.
The article describes the application of the inductive approach to constructing an informational support system for decision-making in the foundry industry. The purpose is to enhance the efficiency of the casting process by making relevant decisions at every stage of the process. The inductive approach is used first of all for modeling the dependence of the cast cooling temperature on parameters of...
The paper considers the constructing issue of ontology for the GMDH-based inductive modeling domain. It examines the main components of the GMDH algorithms in terms of their synthesis for designing the domain ontology. Such ontology significantly expands opportunities for construction of inductive modeling tools for model building and forecast of complex processes of different nature.
An architecture and learning methods for a growing neuro-fuzzy system that enlarges an amount of layers and tunes their synaptic weights in an online way are introduced in the paper. A structure of the hybrid system is built with the help of extended neo-fuzzy neurons which are characterized by improved approximating capabilities. The main peculiar feature of the introduced system is a learning method...
The paper presents theoretical grounds of recurrent-and-parallel computing applying in combinatorial GMDH algorithm for modeling and prediction of complex multidimensional interrelated processes in the class of vector autoregressive models. The effectiveness of the constructed algorithm is demonstrated by modeling of interrelated processes in the field of Ukraine energy sphere with the purpose of...
A new approach to solving the problem of interaction between the main suppliers of electricity and private generating stations of different nature in the island renewable energy micro-grids. This approach can be used for small power islands with different natural conditions, which stipulate the use of solar, wind or other energy sources in order to evaluate the dynamic electricity pricing.
Method for clustering and interval analysis of heterogeneous data sample is considered in the paper and it is shown an algorithm for method implementation. An example of applying of the developed method and the algorithm is represented.
In the paper, the method of parametric identification of interval discrete dynamic models is considered. An improved scheme of the computational implementation of this method is proposed. The scheme considers an area of permissible values for modeled characteristics. Results of the comparative analysis of the proposed scheme efficiency of this method with a known one are presented.
The article describes typical problems solved by means of inductive modeling, provides information on the development of this scientific direction in Ukraine and abroad, characterizes the basic fundamental, applied and technological achievements, and formulates the most promising ways of further research.
One of the key tasks of mobile robotics is navigation, which for Outdoor-type robots is exacerbated by the functioning in a priori of an unknown environment. In this paper, for the first time, the learning navigation system for mobile robot based on inductive modeling approach is presented. This approach is based on the principles of the group method of data handling (GMDH), which is one of the first...
One of the key tasks of Outdoor-type mobile robotics is traversability estimation of underlying surfaces in a in a priori of an unknown heterogeneous environment. The paper presents practical realization of traversability estimation system based on group method of data handling (GMDH). This method is classical technique of data mining and one of the first techniques of Deep Learning. The results of...
Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this paper we show how Inductive algorithms constructed from building blocks on small data sub-sample can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art...
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