Kohonen Self-organizing Network Research Articles

Оценка уровня развития агломерационного эффекта в пространственной социально-экономической системе Санкт-Петербургской агломерации

A methodological approach to assessing the level of development of the agglomeration effect is proposed, tested on the example of administrative units of the largest urban agglomeration in Russia – St. Petersburg. The basis of the methodological approach was relative indicators that made it possible to assess the economic efficiency of the functioning of production activities (profitability), the efficiency of labor activity of the economically active population (labor productivity), the level of formation and spatial distribution of types of economic activity (sectoral uniformity coefficient), the level of spatial heterogeneity of types of economic activity (industry diversity coefficient). Based on the results of calculations of profitability and labor productivity indicators, they were clustered by administrative units of the studied agglomeration using the Kohonen self-organizing network method, which made it possible to determine three groups of levels (high, medium and low) and display their spatial distribution on a cartographic basis. As a result, the highest level of profitability is characteristic of the peripheral region of the St. Petersburg agglomeration – Gatchina, the average level was found in the core of the St. Petersburg agglomeration, as well as in the peripheral Vsevolozhsk, Lomonosov and Kirov regions. The group with low values included Tosnensky and Volosovsky districts, as well as Sosnovoborsky urban district. In terms of labor productivity, a high level was noted in the Lomonosovsky district, an average level in the Sosnovoborsky urban district, in the Vsevolozhsky and Kirovsky districts, and a low level in St. Petersburg, Volosovsky, Gatchina and Tosnensky districts. In terms of sectoral evenness, all administrative units of the studied agglomeration have an average level, and in terms of sectoral diversity, an average level was noted only in St. Petersburg, with a high level in all peripheral regions

Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study

Background: The purpose of the paper is to propose different arrangements of neural networks for short-time 24-h load forecasting in Power Systems. Methods: The study discusses and compares different techniques of data processing, applying the feedforward and recurrent neural structures. They include such networks as multilayer perceptron, radial basis function, support vector machine, self-organizing Kohonen networks, deep autoencoder, and recurrent deep LSTM structures. The important point in getting high-quality results is the composition of many solutions in the common ensemble and their fusion to create the final forecast of time series. The paper considers and compares different methods of fusing the individual results into the final forecast, including the averaging, application of independent component analysis, dynamic integration, and wavelet transformation. Results: The numerical experiments have shown a high advantage of using many individual predictors integrated into the ensemble which are responsible for the final forecast. Especially efficient is the application of non-standard wavelet application in the formation of an ensemble, as well as the use of LSTM as the basic prediction unit. The novelty of the paper is the critical comparative analysis of the time series prediction methods applied for load forecasting in the power system. The presented approach may be useful for the users involved in power system operation management.

Kohonen’s algorithm in problems of classification of defects in printed circuit assemblies

The article presents a new method for diagnosing the technical condition of radio-electronic components, combining the methods of thermal diagnostics with the technologies of artificial neural networks. The structure of the method is shown, and the composition of the functional blocks is determined. The implementation of the method is a symbiosis of technologies for mathematical and simulation modeling of the technical state of a radio-electronic device with its physical tests and research of characteristics. When developing the method, specialized software tools for design and circuit design were actively used, such as Altium Designer CAD, SolidWorks, NI Multisim, the FloTHERM PCB thermal analysis module, as well as the MATLAB mathematical modeling and calculation package. With the help of these tools, a number of studies were carried out, including sets of numerical values of the power of circuit elements and temperature indicators of the printing unit, both for the correct state of the device and in states with artificially introduced defects. They, in turn, became the basis of the database of electronic node failures. To implement diagnostic procedures and identify the technical condition, an artificial neural network based on selforganizing Kohonen maps was created, its structure, parameters and algorithms of functioning were determined. The diagnostic procedure is based on the analysis of information from the fault database and its comparison with experimental data obtained as a result of a physical experiment. The results of the study showed that the network automatically classifies the characteristic defects of electronic components using the algorithms embedded in it. The list of characteristic defects in the proposed diagnostic method is limited to a discrete set of the most common faults, because, as their number increases, the use of the self-organizing Kohonen network for automatic classification becomes much more complicated and ineffective in terms of performance and reliability of identification. Among the advantages of this technology, it should be noted that the Kohonen network has the ability to convert largedimensional input data into a two-dimensional array. So, the results are easy to visualize and convenient to use when generating reports and recommendations for subsequent decision-making about the possibility of using an electronic device.

Применение алгоритмов кластеризации для анализа клиентской базы магазина

This paper analyzes the buyers of the BigCar store, which sells spare parts for trucks, using clustering methods. The algorithms of k-means, g-means, EM and construction of Kohonen networks are considered. For their implementation, the Loginom Community analytical platform is used. Based on sales data for 3 years, buyers are divided into 3 clusters by implementing the k-means, EM algorithms and building a self-organizing Kohonen network. An EM algorithm was also performed with automatic determination of the number of clusters and g-means, which divided buyers into 9 and 10 clusters. The analysis of the resulting clusters showed that the results of the k-means and Kohonen algorithms are better suited to increase sales efficiency.

APPLICATION OF SELF-ORGANIZING KOHONEN MAPS FOR ANALYSIS OF THE PATENT BASE

One of the clustering technologies is considered - self-organizing Kohonen networks, bottlenecks for data analysis with similar algorithms are analyzed. The general problems of the adaptation of mathematical models and the applicability of the clustering algorithms themselves are touched upon.The classification problem is one of the most ancient problems, the essence of which is to divide the set of objects under study into homogeneous groups in a certain sense. The basis for the classification is dictated by the nature of what we are classifying, although sometimes it is necessary to take as the basis such metrics for which there are objective ways to measure them.You also need to clearly distinguish between classification and typology, the latter is much broader. Typology is understood as a method of scientific knowledge, based on the dismemberment of objects and their grouping using a generalized, idealized model or type.

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks.

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

Prospects for the implementation of neural network technologies in radar information processing systems

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

ПРОГНОЗИРОВАНИЕ СВОЙСТВ МНОГОКОМПОНЕНТНОЙ КЕРАМИКИ НА ОСНОВЕ САМООРГАНИЗУЮЩЕЙСЯ СЕТИ КОХОНЕНА

Рассмотрены информационные возможности метода прогнозирования значений многомерных экспериментальных данных, определенных на неравномерной сетке. Суть метода заключается в использовании самоорганизующейся сети Кохонена для определения качественного влияния независимых параметров в многокомпонентной керамике на основе оксида олова на коэффициент ее нелинейности в задаче улучшения параметров элементов защиты электрических цепей.

Belemnites of the Neobelemnella kazimiroviensis group – a comparison of the self-organizing Kohonen networks algorithm with the classic palaeopopulation statistical approach and approaching their origin

Belemnites of the Neobelemnella kazimiroviensis group were classified applying the Artificial Neural Networks method, the self-organizing Kohonen algorithm. Four species are distinguished, Neobelemnella kazimiroviensis (Skołozdrówna, 1932), Neobelemnella pensaensis (Naidin, 1952), Neobelemnella skolozdrownae (Kongiel, 1962), and Neobelemnella aff. kazimiroviensis (Skołozdrówna, 1932). The first two species occur in the Upper Maastrichtian of Central Asia (Kazakhstan, Turkmenistan), Russia, Poland, Denmark, the Netherlands and Belgium. N. skolozdrownae is limited to Poland, Denmark, the Netherlands and Belgium, while N. aff. kazimiroviensis occurs in the Volga Basin (Russia) and Kazakhstan. The evolution of the N. kazimiroviensis group from a member of the Belemnella praearkhangelskii group of Central Russia or Kazakhstan or from the Belemnitella americana group from New Jersey (USA) is discussed applying Hierarchical Cluster Analysis and Multidimensional Comparative Analysis. A member of the Bt. americana group – ? Neo-belemnella subfusiformis (Whitfield, 1892) – is referred to the genus Neobelemnella Naidin, 1975 albeit with a query. This supports the hypothesis that the N. kazimiroviensis group could have evolved from a North American precursor.

Identification of plants condition using digital images

The paper deals with the problem of evaluation of the current state of plants by digital images using color and morphological descriptors. The above mentioned problem is widely used in automated greenhouse control systems. The paper presents a procedure for identification of the state of plants. The questions of segmentation and morphological image processing are considered. Self-organizing Kohonen networks and feed-forward networks are used as classifiers of RGB images and geometric descriptors. The use of the approach described in the paper allows remote assessing the condition of plants in greenhouses, increasing productivity and reducing the percentage of crop losses.

Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

This paper presents a comparative study on the application of different neural network structures to early detection of electrical faults in induction motor drives. The diagnosis inference of the stator inter-turn short-circuits and broken rotor bars is based on the analysis of an axial flux of the induction motor. In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. In order to assess the sensitivity of the applied neural detectors, the tests were carried out for variable load conditions and for different values of the supply voltage frequency. Experimental results of the elaborated neural detectors are presented and discussed.

The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling

At the present time, spatial data are often acquired using varied remote sensing sensors and systems, which produce big data sets. One significant product from these data is a digital model of geographical surfaces, including the surface of the sea floor. To improve data processing, presentation, and management, it is often indispensable to reduce the number of data points. This paper presents research regarding the application of artificial neural networks to bathymetric data reductions. This research considers results from radial networks and self-organizing Kohonen networks. During reconstructions of the seabed model, the results show that neural networks with fewer hidden neurons than the number of data points can replicate the original data set, while the Kohonen network can be used for clustering during big geodata reduction. Practical implementations of neural networks capable of creating surface models and reducing bathymetric data are presented.

A KSOM based neural network model for classifying the epilepsy using adjustable analytic wavelet transform

Epilepsy is a nervous disorder occurring in the cerebral cortex location of the brain which is caused by irregular harmonization of neurons. Since the existence of this disorder is between the neurons, it is tedious to diagnose correctly. Research works of epilepsy mostly done on an Electroencephalogram (EEG) signals for analyzing the neuron activity of the brain during seizures. Analyzing the continuing EEG reports manually for a patient affected by epilepsy is time-consuming, and it needs a large storage volume. The proposed paper is based on a unique method for detecting epileptic seizures by Adjustable Analytic Wavelet Transform (AAWT). This work is also focused on testing the practicability of utilizing the Kohonen network maps for predicting the dynamics of the brain states in the form of the trajectory which may provide the occurrence of the seizure event. AAWT is applied on each EEG signal to decompose EEG signals into the sub-band signals. The fractal dimension is applied to these sub-bands signals as a discriminating feature due to its nonlinear chaotic trait. The received solutions are fed into Kohonen self-organizing network map (KSOM) to get a stable performance rate for the categorization of an epileptic seizure. The results proved that the introduced methodology achieved 98.72% sensitivity, 93.90% specificity, 93.03% selectivity, and 94.12% efficiency than the existing models and provided promising classification accuracy.

Development of module for neural network identification of attacks on applications and services in multi-cloud platforms

The article presents the results of developing an approach to detecting and protecting against network attacks on the corporate infrastructure deployed on the multi-cloud platform. The proposed approach is based on the combination of two technologies: a softwareconfigurable network and virtualization of network functions. The approach for searching for anomalous traffic is to use a hybrid neural network consisting of a self-organizing Kohonen network and a multilayer perceptron. The study of the work of the prototype of the system for detecting attacks, the method of forming a learning sample, and the course of experiments are described. The study showed that using the proposed approach makes it possible to increase the effectiveness of the obfuscation of various types of attacks and at the same time does not reduce the performance of the network

Methods for automatic cloud classification from MODIS data

In this paper, different texture-analysis methods are used to describe different cloud types in MODIS satellite images. A universal technique is suggested for the formation of efficient sets of textural features using the algorithm of truncated scanning of the features for different classifiers based on neural networks and cluster-analysis methods. Efficient sets of textural features are given for the considered classifiers; the cloud-image classification results are discussed. The characteristics of the classification methods used in this work are described: the probabilistic neural network, K-nearest neighbors, self-organizing Kohonen network, fuzzy C-means, and density clustering algorithm methods. It is shown that the algorithm based on a probabilistic neural network is the most efficient. It provides for the best classification reliability for 25 cloud types and allows the recognition of 11 cloud types with a probability greater than 0.7. As an example, the cloud classification results are given for the Tomsk region. The classifications were carried out using full-size satellite cloud images and different methods. The results agree with each other and agree well with the observational data from ground-based weather stations.

Advances in Artificial Neural Networks – Methodological Development and Application

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.

Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system

The primary objective of group technology (GT) is to enhance the productivity in batch manufacturing environment. The GT cell formation and fractional cell formation are done by using Kohonen self-organizing map (KSOM) networks. The effectiveness of the cell formation is measured with number of exceptional elements, bottleneck parts and grouping efficiency and the effectiveness of the fractional cell formation is measured by number of exceptional elements and the number of machines in the reminder cell. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or best when compared to the other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

The use of Markovian metapopulation models: Reducing the dimensionality of transition matrices by self-organizing Kohonen networks

Markovian population models are used in conservation biology to find an accurate estimate of a population's extinction probability. Such models require handling of large transition matrices and calculations are thus extremely time-consuming when large populations have to be studied. To accomplish these problems, some authors have suggested to group together several states/sizes of the population. Unfortunately, this so-called binning frequently results in errors in estimates obtained. The main problem with binning is that it assumes that grouped states behave nearly identical with respect to the underlying stochastic population process and that so far binning methods implicitly violate this assumption. In this paper, we present a new binning method based on self-organizing Kohonen neural networks for time-homogeneous Markovian metapopulation models. The neural networks are used to analyse one-step transitions of the Markov chain in order to group only nearly identical states. We show that the new method is more qualified for the use in conservation than deterministic methods that we had discussed in a previous paper (first order Fibonacci binning, pairs binning). It reveals more accurate and more reliable estimates than these methods. Errors in estimated extinction probabilities that were introduced by binning did not exceed one order of magnitude and errors in the global population size did not exceed 30%. These errors in estimates correspond to a low inaccuracy in model parameter values of population growth and migration ranging from ±1 to ±10%. The reduction in the state space of the studied metapopulations ranged from 21 to 33% per subpopulation. The resulting decrease in computing time caused by our binning method is substantial particularly with regard to simulations tasks such as comparing the extinction risk of several populations or performing a detailed sensitivity analysis for model parameters assumed. The successful estimation of the extinction risk of eight natural butterfly populations demonstrates the applicability of our new binning method in conservation biology. A comparison of extinction probabilities and mean population sizes estimated by Monte Carlo simulations [Griebeler, E.M., Seitz, A., 2002. An individual based model for the conservation of the endangered Large Blue butterfly, Maculinea arion (Lepidoptera: Lycaenidae). Ecol. Model. 134, 343–356] with those obtained from the respective Markov chains for these butterfly populations revealed similar results on the accuracy of estimates and the reduction in transition matrices that were predicted by the comparative error analysis for binning methods.

Fourier and wavelet descriptors for shape recognition using neural networks—a comparative study

This paper presents the application of three different types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of different neural network structures associated with different preprocessing of the data results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. The integration of the individual classifiers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments.

Extending the Kohonen self-organizing map networks for clustering analysis

The self-organizing map (SOM) network was originally designed for solving problems that involve tasks such as clustering, visualization, and abstraction. While Kohonen's SOM networks have been successfully applied as a classification tool to various problem domains, their potential as a robust substitute for clustering and visualization analysis remains relatively unresearched. We believe the inadequacy of attention in the research and application of using SOM networks as a clustering method is due to its lack of procedures to generate groupings from the SOM output. In this paper, we extend the original Kohonen SOM network to include a contiguity-constrained clustering method to perform clustering based on the output map generated by the network. We compare the result with that of the other clustering tools using a classic problem from the domain of group technology. The result shows that the combination of SOM and the contiguity-constrained clustering method produce clustering results that are comparable with that of the other clustering methods. We further test the applicability of the method with two widely referenced machine-learning cases and compare the results with that of several popular statistical clustering methods.