Optimization of a multi-band reconfigurable microstrip line-fed rectangular patch antenna using Self-Organizing Maps

Remote sensing of terrestial and planetary surfaces in the infrared for the purpose of identifying minerals and their distribution is an ongoing activity. The huge amount of spectral data currently available and expected from future space missions presents a challenge to the scientist determine to extract useful scientific information from this data. Automated methods to facilitate this process are clearly needed to complete these investigations in a timely manner. Organizing this data into meaningful chunks is a crucial first step of this analysis that can be be addressed with clustering techniques. We have developed an automated clustering and classification scheme based on Kohonen Self-Organizing maps (SOM). The SOM is a type of unsupervised neural network widely used to cluster data and identify anomalies. A modification of the Davies-Bouldin (DB) cluster validation index which incorporates the measurement uncertainties of the SOM training data is used to determine the optimal number of clusters. This scheme was trained and tested with the mineral spectral libraries prepared at Arizona State University (ASU) and John Hopkins University (JHU) whose samples have been hierarchically labeled with Class, Subclass and Group names. These names are used to measure the spectral purity of the derived clusters and the accuracy of classification. We describe in detail the SOM scheme itself, the performance measures, and testing procedure. The test results demonstrate that the SOM can be a useful component in autonomous systems designed to identify minerals from reflectance or emissivity measurements in the thermal infrared.

QuestionCan a Kohonen's self‐organizing map, which is robust to non‐linear relationships between variables and their non‐normal distributions, be effective in patterning the data on plant communities investigated with the classical Braun‐Blanquet phytosociological method based on the ordinal scale? Does the application of the self‐organizing map make it possible to obtain new information from the analysed plant communities when compared to the Braun–Blanquet method alone?LocationThe Babsk nature reserve, Central Poland.MethodsThe analysed data were derived from two separate series of phytosociological studies on plant communities dating from periods 31 yr apart (1960 and 1991). The data consisted of 24 quantitative sampling lists of plant species (=phytosociological releves). The releves were analysed by the application of a Kohonen's self‐organizing map and the indicator value (IndVal).ResultsThe transformations from Querco roboris–Pinetum to Tilio cordatae–Carpinetum betuli in the vegetation in the Babsk nature reserve over 31 yr were determined precisely. Valuable new data were also obtained on: (1) significant associations of individual plant species with the previous and recent phytocoenoses, i.e. diagnostic groups of plant species for each phytocoenosis; (2) abiotic conditions (determined retrospectively in the two study periods on the basis of ecological indicators for vascular plants); and (3) the concordance of phytocoenoses with the biotope.ConclusionsThe Kohonen's self‐organizing map method and the IndVal made it possible to efficiently identify abiotic and biotic patterns for plant communities on the basis of the data expressed in the conventional Braun‐Blanquet scale.

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

Spiking Self-organizing Maps for Classification Problem

Kohonen's self-organizing map (SOM), which performs topology-preserving transformation from a high dimensional data vector space to a low-dimensional map space, provides a powerful tool for data analysis, classification and visualization in many application fields. Despite its power, SOM can only deal with vectorized data, although many expansions have been proposed for various data-type cases. This study aims to develop a novel generalization of SOM called modular network SOM (mnSOM), which enables users to deal with general data classes in a consistent manner. mnSOM has an array structure consisting of function modules that are trainable neural networks, e.g. multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM family. In the case of MLP-modules, mnSOM learns a group of systems or functions in terms of the input-output relationships, and at the same time, mnSOM generates a feature map that shows distances between the learned systems. Thus, mnSOM with MLP modules is an SOM in function space rather than in vector space. From this point of view, the conventional SOM of Kohonen's can be regarded as a special case of mnSOM, the modules consisting of fixed-value bias units. In this paper, mnSOM with MLP modules is described along with some application examples.

Identification of lithofacies using Kohonen self-organizing maps

Describes a series of experiments on using Kohonen self-organizing maps and hybrid systems for continuous speech recognition. Experiments with different nonlinear transformations on the signal before using a neural network has been done and results compared. The hybrid system developed by the authors combines self-organizing feature maps with dynamic time warping. The experiments suggest that the combination has better performance than either of the two methods applied individually. >

It is objective and practical for an electric machinery and apparatus test to computerize its diagnosis process with fault location identification. It is possible to recognize a fault location from measured voltage and current information by using a Kohonen's self-organizing map (SOM) which only needs a learning process of relation between the locations and their voltage and current characteristics. Diagnosis methods for some types of apparatus are proposed by using the SOM. First a diagnosis method for a three-phase PWM inverter which may have a switch fault is proposed. Twelve types of switch fault patterns are analyzed for its DC and harmonic components which are used as input data for their diagnosis map. The method is also applied to a multi-level, for example, three-level inverter. Secondly a diagnosis method of a linear induction motor which may have a turn or a turn-to-turn fault is proposed. Diagonal impedance for all fault cases under considerations are used for the SOM learning process and they are checked for their fault detections. Finally a diagnosis method for an induction machine which may have a turn fault is proposed. An impedance, which is a small slip-dependent term and expresses the degree of motor asymmetry, is computed from two sets of applied input voltages and currents and used as SOM input data. A simple mathematical model of an induction motor with the reference frame transformation is investigated. The above application examples are all validated for its simulated results by developed Matlab programs.

The purpose of this article is to investigate customer clustering based on big data of the consumer loyalty system. The object of the research is the retail chains that are the clients of the Maxbonus system. Self-organizing Kohonen maps, implemented by the selforgmap function of the Deep Learning Toolbox module of the Matlab system, were used as a clustering tool. As a result of clustering 990 customers, classes invariant to the partition were identified, and it was shown that their number stabilizes with an increase in the number of classes in the Kohonen self-organizing map. The average indicators of representatives of each class have been determined. This result indicates the efficiency of the approach for clustering customers of customer loyalty systems. The prospect of the work is to include in the number of input parameters clustering the volume of purchases by product categories. Also promising is the transition to clustering retail chains participating in the Maxbonus system.

Artificial neural network analysis of noisy visual field data in glaucoma

Contribution of the Physicochemical Properties of Active Pharmaceutical Ingredients to Tablet Properties Identified by Ensemble Artificial Neural Networks and Kohonen's Self-Organizing Maps

Objects and processes classification is a common experimental problem. Its solution, first of all, is needed in automatic diagnosis systems, for example, to determine the equipment operation state through the diagnostic signal or to identify abnormalities with medical images. With the development of convolutional neural networks, new prospects for solving such problems have risen up. However, the classification accuracy that can be achieved on these networks is not sufficient enough for all diagnosis issues. It is subject to, for example, timely diagnosis of the onset of transient phenomena. At the same time, another type of neural network, Kohonen self-organizing maps, has a conceptual property for training on unclassified set of classes, that is, giving the opportunity to solve such an issue. Therefore, the accuracy enhancement of the classification on the basis of Kohonen networks implementation in the architecture of convolutional networks is a relevant objective and has practical significance. The article analyzes the means of improving the accuracy of convolutional neural networks and arising problems solution. The ways of increasing the proportion of correct clustering on Kohonen networks due to the ‘growth’ of its grid shape in determining new classes in the learning process are also given. It is this property that makes it possible to recognize transient phenomena. It is determined that the existing solutions of the combination of Kohonen networks and convolutional networks are aimed at improving the efficiency of only self-organizing maps, with the purpose to improve the accuracy of classification by convolutional networks, it became necessary to develop a new architecture. The paper provides a description of this issue. Since the initial information of the Kohonen networks is the weighting matrix values of the grid shape neurons, it was necessary to associate it with the representation of the images in order to process the diagnostic images. The paper proposes the concept of a built-in associative array block based on Kohonen networks. According to the proposed method, a software implementation of a hybrid neural network is developed. The formulation and results of computational experiments are presented. The efficiency of the proposed method is experimentally proved. Keywords: convolutional neural networks, CNN, Kohonen self-organizing maps, SOM, signal classification, image classification.

Soft classification using Kohonen's Self-Organizing Map (SOM) has not been explored as thoroughly as the Multi-Layer-Perceptron (MLP) neural network. In this paper, we propose two non-parametric algorithms for the SOM to provide soft classification outputs. These algorithms, which are labelling-frequency-based, are called SOM Commitment (SOM-C) and SOM Typicality (SOM-T), expressing in the first case the degree of commitment the classifier has for each class for a specific pixel and in the second case, how typical that pixel's reflectances are of those upon which the classifier was trained. To evaluate the two proposed algorithms, soft classifications of a Satellite Pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) image and an Airborne Visible Infrared Imaging Spectrometer (AVIRIS) image were undertaken. Both traditional soft classifiers, i.e. Bayesian posterior probability and Mahalanobis typicality classifier, and the most frequently used non-parametric neural network model, i.e. MLP, were used as a comparison. Principal-components analysis (PCA) was used to explore the relationship between these measures. Results indicate that great similarities exist between the SOM-C, MLP and the Bayesian posterior probability classifiers, while the SOM-T corresponds closely with Mahalanobis typicality probabilities. However, as implemented, they have the advantage of being non-parametric. The proposed measures significantly outperformed Bayesian and Mahalanobis classifiers when using the hyperspectral AVIRIS image.

<b><sc>Abstract.</sc></b> The area irrigated by furrow irrigation in the U.S. has been steadily decreasing but still represents about 20% of the total irrigated area in the U.S. Furrow irrigation sediment loss is a major water quality issue in the western U.S. and a method for estimating sediment loss is needed to quantify the environmental impacts and estimate effectiveness and economic value of conservation practices. The objective of the study was to investigate the use of the unsupervised machine learning technique Kohonen self-organizing maps (KSOM) to predict furrow sediment loss. Historical published and unpublished data sets containing measurements of furrow irrigation sediment loss in the western U.S. were assembled into a furrow sediment loss data set comprising over 2000 measurements. Despite the immunity of KSOMs to measurement variability, the inherent variability in measured furrow sediment loss limited the ability of a KSOM model to reliability predict furrow sediment loss. Furrow sediment loss was under predicted by 44% on average with a linear regression coefficient of determination of 0.6. The KSOM model was placing little weight on measured sediment loss in the input data set, indicating that it was clustering the data based on input parameters defining the hydraulic and soil conditions. This outcome was used to develop a transfer learning approach for predicting furrow sediment loss. The transfer learning approach used a KSOM to cluster data records of similar hydraulic and soil conditions in the data set. Mean measured sediment loss and furrow flow rate of each cluster was determined based on data set vectors assigned to a cluster by the KSOM. Furrow sediment loss prediction was obtained by applying an input vector to the KSOM to identify the cluster the input vector most closely matches. Then the mean measured sediment loss of the identified cluster was adjusted for any difference between the input vector furrow flow rate and cluster mean furrow flow rate to obtain a prediction of furrow sediment loss. Predicted furrow sediment loss was 16% less than measured sediment loss on average with a coefficient of determination of 0.82. When the data set was randomly split into model development (90%) and validation (10%) data sets the prediction results were similar.

A challenging and daunting task for financial investors is determining stock market timing - when to buy, sell and the future price of a stock. This challenge is due to the complexity of the stock market. New methods have emerged that increase the accuracy of stock prediction. Examples of these methods are fuzzy logic, neural network and hybridized methods such as hybrid Kohonen self organizing map (SOM), adaptive neuro-fuzzy inference system (ANFIS) etc. This paper presents a number of methods used to predict the stock price of the day. These methods are backpropagation, Kohonen SOM, and a hybrid Kohonen SOM. The results show that the difference in error of the hybrid Kohonen SOM is significantly reduced compared to the other methods used. Hence, the results suggest that the hybrid Kohonen SOM is a better predictor compared to Kohonen SOM and backpropagation