Edge detection in multispectral images using the self-organizing map

In this paper, two new methods for edge detection in multispectral images are presented. They are based on the use of the self-organizing map (SOM) and a grayscale edge detector. With the 2-dimensional SOM the ordering of pixel vectors is obtained by applying the Peano scan, whereas this can be omitted using the 1-dimensional SOM. It is shown that using the R-ordering based methods some parts of the edges may be missed. However, they can be found using the proposed methods. Using them it is also possible to find edges in images which consist of metameric colors. Finally, it is shown that the proposed methods find the edges properly from real multispectral airplane images. The size of the SOM determines the amount of found edges. If the SOM is taught using a large color vector database, the same SOM can be utilized for numerous images.     

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper,a new medical image classification scheme is proposed using selforganizing map(SOM)combined with multiscale technique.It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers.First,to solve the difficulty in manual selection of edge pixels,a multiscale edge detection algorithm based on wavelet transform is proposed.Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set.In this new scheme,the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously.On the other hand,the misclassification of the traditional multiscale SOM classifier in regions near edges is graeatly reduced and the correct classification is improved at the same time.     

自组织多模型逼近性能分析与改进

针对基于自组织映射神经网络的非线性函数逼近,研究其方法和原理,指出它与一般前向神经网络在逼近原理上的不同．在此基础上,进一步研究该方法的逼近性能,分析其两个不足之处,进而提出一种提高逼近性能的改进神经网络训练策略．最后通过仿真实例验证了所得结论,表明了改进方法的有效性．     

Spatiotemporal evolution analysis of time-series land use change using self-organizing map to examine the zoning and scale effects

In this study, we applied a self-organizing map (SOM) neural network method to analyze the spatiotemporal evolution of land-use in Beijing using five time-period classification data from 2005 to 2013. We conducted a spatiotemporal integrated expression and a comparative analysis of the time-series of land use data at 5 km grid level. The experiments at the township level and three different grid levels (20 km, 10 km and 1 km) were simultaneously conducted as the comparison study to analysis the modifiable areal unit problem (MAUP). The land use structure data of analysis unit over 5 years were used as input data for SOM. After training the SOM network, the aggregation modes for different land use types were identified on the output plane. Then, the second-step cluster of the output neurons of the SOM was analyzed to construct a series of land use change trajectories that enabled us to get the spatiotemporal patterns of land use change. The results showed five spatial aggregation patterns and three spatiotemporal change patterns of land use 2005 to 2013. The three patterns of spatiotemporal change represent (1) the expansion of urban areas onto farmland in the southeast plains, (2) the development of forest land in the northwest mountainous areas, and (3) the development of piedmont mixed type land use structures. The results of the comparison experiments showed the zoning effect and the scale effect of MAUP, which were: the 5 km grid-based analysis could provide more precise spatiotemporal evolution patterns in the mountainous area, whereas the township level analysis was more appropriate in the plain area; the pattern of forest land development could be better revealed on 20 km and 10 km grid level, while the pattern of built-up land development could be better revealed on 5 km and 1 km grid level.     

The time-organized map algorithm: extending the self-organizing map to spatiotemporal signals

The new time-organized map (TOM) is presented for a better understanding of the self-organization and geometric structure of cortical signal representations. The algorithm extends the common self-organizing map (SOM) from the processing of purely spatial signals to the processing of spatiotemporal signals. The main additional idea of the TOM compared with the SOM is the functionally reasonable transfer of temporal signal distances into spatial signal distances in topographic neural representations. This is achieved by neural dynamics of propagating waves, allowing current and former signals to interact spatiotemporally in the neural network. Within a biologically plausible framework, the TOM algorithm (1) reveals how dynamic neural networks can self-organize to embed spatial signals in temporal context in order to realize functional meaningful invariances, (2) predicts time-organized representational structures in cortical areas representing signals with systematic temporal relation, and (3) suggests that the strength with which signals interact in the cortex determines the type of signal topology realized in topographic maps (e.g., spatially or temporally defined signal topology). Moreover, the TOM algorithm supports the explanation of topographic reorganizations based on time-to-space transformations (Wiemer, Spengler, Joublin, Stagge, & Wacquant, 2000).     

Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map

Vision based redundant manipulator control with a neural network based learning strategy is discussed in this paper. The manipulator is visually controlled with stereo vision in an eye-to-hand configuration. A novel Kohonen’s self-organizing map (KSOM) based visual servoing scheme has been proposed for a redundant manipulator with 7 degrees of freedom (DOF). The inverse kinematic relationship of the manipulator is learned using a Kohonen’s self-organizing map. This learned map is shown to be an approximate estimate of the inverse Jacobian, which can then be used in conjunction with the proportional controller to achieve closed loop servoing in real-time. It is shown through Lyapunov stability analysis that the proposed learning based servoing scheme ensures global stability. A generalized weight update law is proposed for KSOM based inverse kinematic control, to resolve the redundancy during the learning phase. Unlike the existing visual servoing schemes, the proposed KSOM based scheme eliminates the computation of the pseudo-inverse of the Jacobian matrix in real-time. This makes the proposed algorithm computationally more efficient. The proposed scheme has been implemented on a 7 DOF PowerCube? robot manipulator with visual feedback from two cameras.     

Identification and control of dynamical systems using the self-organizing map.

In this paper, we introduce a general modeling technique, called vector-quantized temporal associative memory (VQTAM), which uses Kohonen's self-organizing map (SOM) as an alternative to multilayer perceptron (MLP) and radial basis function (RBF) neural models for dynamical system identification and control. We demonstrate that the estimation errors decrease as the SOM training proceeds, allowing the VQTAM scheme to be understood as a self-supervised gradient-based error reduction method. The performance of the proposed approach is evaluated on a variety of complex tasks, namely: i) time series prediction; ii) identification of SISO/MIMO systems; and iii) nonlinear predictive control. For all tasks, the simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network. The SOM has also shown to be less sensitive to weight initialization than MLP networks. We conclude the paper by discussing the main properties of the VQTAM and their relationships to other well established methods for dynamical system identification. We also suggest directions for further work.     

The memetic self-organizing map approach to the vehicle routing problem

The paper presents an extension of the self- organizing map (SOM) by embedding it into an evolutionary algorithm to solve the Vehicle Routing Problem (VRP). We call it the memetic SOM. The approach is based on the standard SOM algorithm used as a main operator in a population based search. This operator is combined with other derived operators specifically dedicated for greedy insertion moves, a fitness evaluation and a selection operator. The main operators have a similar structure based on the closest point findings and local moves performed in the plane. They can be interpreted as performing parallels and massive insertions, simulating the behavior of agents which interact continuously, having localized and limited abilities. This self-organizing process is intended to allow adaptation to noisy data as well as to confer robustness according to demand fluctuation. Selection is intended to guide the population based search toward useful solution compromises. We show that the approach performs better, with respect to solution quality and/or computation time, than other neural network applications to the VRP presented in the literature. As well, it substantially reduces the gap to classical Operations Research heuristics, specifically on the large VRP instances with time duration constraint.     

Condition monitoring of the cutting process using a self-organizing spiking neural network map

This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map. Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these spikes to encode and compute information in a more efficient way. The paper introduces the basis of a simplified version of the Self-Organizing neural architecture based on Spiking Neurons. The fundamental steps for the development of this computational model are presented as well as some experimental evidence of its performance. It is shown that this computational architecture has a greater potential to unveil embedded information in tool wear monitoring data sets and that faster learning occurs if compared to traditional sigmoidal neural networks.     

Some measures to impact on the performance of Kohonen self-organizing map

Multimedia Tools and Applications - In the field of unsupervised learning, Self-Organizing Map (SOM) has attracted the attention of many researchers. SOM is a popular algorithm in the area of data...     

Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map

Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.     

A directed batch growing approach to enhance the topology preservation of self-organizing map

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without pre-determining their size. Most of the proposed growing SOM algorithms use an incremental learning strategy. The conventional growing approach of GSOM is based on filling all available position around the candidate neuron which can decrease the topology preservation quality of the map due to the misconfiguration and twisting of the map which could be a consequence of unexpected network growth and improper neuron addition and weight initialization. To overcome this problem, in this paper we introduce a batch learning strategy for growing self-organizing maps called DBGSOM which direct the growing process based on the accumulative error around the candidate boundary neuron. In the proposed growing approach, just one new neuron is added around each candidate boundary neuron. The DBGSOM offers suitable mechanisms to find a proper growing positions and allocating initial weight vectors for the new neurons.The potential of the DBGSOM was investigated with one synthetic dataset and six real-world benchmark datasets in terms of topology preservation and mapping quality. Experimental results showed that the proposed growing strategy provides an enhanced topology preserved map and reduces the susceptibility of twisting compared to GSOM. Furthermore, the proposed method has a better clustering ability than GSOM and SOM. According to the lower number of neurons generated by DBGSOM, it needs less time to learn the manifold of the data points compared to GSOM.     

Modeling and simulation of the open-end equity mutual fund market in Taiwan by using self-organizing map

This research applies artificial intelligence (AI) of unsupervised learning self-organizing map neural network (SOM-NN) to establish a model to select the superior funds. This research period is from year 2000 to 2010 and picks 100 domestic equity mutual funds as study object. This research used 30 days prior to the beginning of each month’s prior 30 days, 60 days, 90 days on fund’s net asset value and the Taiwan Weighted Stock Index (TAIEX) return as the fund’s relative performance evaluation indicators classified by month. Finally, based on the superior rate or the average return rate, this research select the superior funds and simulate investment transactions according to this model.The empirical results show that using the mutual fund’s net asset value and the TAIEX’s relative return as SOM-NN input variables not only finds out the superior fund but also has a good predictive ability. Applying this model to simulate investment transactions will be better than the random trading model and market. The experiments also found that the investment simulation of a three-month interval has the highest profitability. The model operation suggests that it is more suitable for short-term and medium-term investment. This research can assist investors in making the right investment decisions while facing rapid financial environment changes.     

Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density

The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters.     

Evaluation method of the gait motion based on self-organizing map using the gravity center fluctuation on the sole

This paper describes the evaluation method of the gait motion in walk rehabilitation. We assume that the evaluation consists of the classification of the measured data and the prediction of the feature of the gait motion. The method may enable a doctor and a physical therapist to recognize the condition of the patients more easily, and increase the motivation of patient further for rehabilitation. However, it is difficult to divide the gait motion into discrete categories, since the gait motion continuously changes and does not have the clear boundaries. Therefore, the self-organizing map (SOM) that is able to arrange the continuous data on the almost continuous map is employed in order to classify them. And, the feature of the gait motion is predicted by the classification. In this study, we adopt the gravity-center fluctuation (GCF) on the sole as the measured data. First, it is shown that the pattern of the GCF that is obtained by our developed measurement system includes the feature of the gait motion. Secondly, the relation between the pattern of the GCF and the feature of the gait motion that the doctor and the physical therapist evaluate by visual inspection is considered using the SOM. Next, we describe the prediction of following features measured by numerical values: the length of stride, the velocity of walk and the difference of steps that are important for the doctor and the physical therapist to make a diagnosis of the condition of the gait motion in walk rehabilitation. Finally, it is investigated that the position of a new test data that is arranged on the map accords with the prediction. As a consequence, we confirm that the method using the SOM is often useful to classify and predict the condition of the patient.     

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

The studies of impervious surfaces are important because they are related to many environmental problems, such as water quality, stream health, and the urban heat island effect. Previous studies have discussed that the self-organizing map (SOM) can provide a promising alternative to the multi-layer perceptron (MLP) neural networks for image classification at both per-pixel and sub-pixel level. However, the performances of SOM and MLP have not been compared in the estimation and mapping of urban impervious surfaces. In mid-latitude areas, plant phenology has a significant influence on remote sensing of the environment. When the neural networks approaches are applied, how satellite images acquired in different seasons impact impervious surface estimation of various urban surfaces (such as commercial, residential, and suburban/rural areas) remains to be answered. In this paper, an SOM and an MLP neural network were applied to three ASTER images acquired on April 5, 2004, June 16, 2001, and October 3, 2000, respectively, which covered Marion County, Indiana, United States. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R²) were calculated to indicate the accuracy of impervious surface maps. The results indicated that the SOM can generate a slightly better estimation of impervious surfaces than the MLP. Moreover, the results from three test areas showed that, in the residential areas, more accurate results were yielded by the SOM, which indicates that the SOM was more effective in coping with the mixed pixels than the MLP, because the residential area prevailed with mixed pixels. Results obtained from the commercial area possessed very high RMSE values due to the prevalence of shade, which indicates that both algorithms cannot handle the shade problem well. The lowest RMSE value was obtained from the rural area due to containing of less mixed pixels and shade. This research supports previous observations that the SOM can provide a promising alternative to the MLP neural network. This study also found that the impact of different map sizes on the impervious surface estimation is significant.     

Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model

The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond 1 year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.     

Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity

Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.

     

On the algebraic fitting of experimental thermodynamic data with special regard to employing of orthonormal polynomials

Following the Weierstrass approximation theorem the thermodynamic excess functions are representable with arbitrary high accuracy by means of polynomials of sufficient high degrees in the mole fraction x. So, algebraic fitting of experimental thermodynamic excess data can be based upon mathematical polynomial expressions without any loss of generality. With respect to the necessary scattering of experimental results, algebraic evaluation of those data can only be solved by employing the calculus of observations. The least square method is the only principle of fitting with full justification by statistical mathematics, and which can be applied directly for algebraic fitting of experimental data by means of a computer. The general linear problem of fitting is solved explicitly (i) by means of Gauss method of elimination, and (ii) by employing the property of “orthonormality” of polynomials. In the latter case the explicit form of the “orthonormal” polynomials depends strongly on the number of experimental data which has to be fitted. A convenient procedure is presented to generate polynomials which are orthonormal with respect to an actual set of experimental data. Computer-programs in PORTRAN-language are enclosed 1) to employ Gauss method of elimination, and 2) to generate discrete orthonormal polynomials.