GK-Means SOM Algorithm Used to Plan the Paths for Multiple Agents Exploring Multiple Target Points

Extending the Kohonen self-organizing map networks for clustering analysis

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.

The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight, and the cluster center is the “food” of the particle group. Each particle moves toward the nearest cluster center. Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration. After a lot of experimental analysis on the commonly used UCI data set, this paper not only solves the shortcomings of K-means clustering algorithm, the problem of dependence of the initial clustering center, and improves the accuracy of clustering, but also avoids falling into the local optimum. The algorithm has good global convergence.

Binary tree time adaptive self-organizing map

During the long-term operation of wind turbines, due to environmental factors and equipment aging, the health and reliability of each component will gradually decline, leading to failure. To assess the health status of wind turbines, timely grasp the subsequent changes and development trends, it is necessary to extract degradation characteristics, including time domain, frequency domain, and time-frequency domain characteristics. These degradation characteristics can reflect the operating status of the equipment, help build health indicator curves, and evaluate the health status of high-speed shaft bearings of wind turbines. Selecting reasonable degradation characteristics is an important prerequisite for constructing a health index curve, and using evaluation indicators to construct a comprehensive evaluation function to screen degradation characteristics. The feature fusion method based on a self-organizing feature mapping network is used to fuse multiple selected degradation features and fuse the selected multiple degradation features into a curve that can reflect the bearing degradation process. Finally, a quantitative analysis is performed on the health index curve to scientifically assess the health status of bearings. Bearings are one of the key components of wind turbines. Based on the health index curve constructed in this article, an appropriate prediction model is selected to predict the health index trend of bearings. A timely and effective grasp of the health trends of wind turbine bearings is of great practical significance for formulating scientific and reasonable maintenance measures for wind farms. The work of this article will be divided into the following four parts: (1) Extracting the time domain, frequency domain, and time-frequency domain degradation characteristics of high-speed shaft bearing vibration signals of wind turbines; (2) Comprehensive evaluation using monotonicity, correlation, and robustness constructs function to screen degenerate features; (3) Use self-organizing feature mapping network. The network integrates the selected degradation features and constructs a health index curve; (4) Based on the constructed health index curve, optimize the BiLSTM network hyperparameters through Bayesian and establish a BO-BiLSTM network model to achieve a more accurate and scientific prediction of the health index trend.

The transient caused by the load/capacitor switching is one of the current important power quality (PQ) problems. Especially, the capacitor switching on may lead to the system parallel resonance. The PQ monitoring, on the other hand, is addressed to identify different PQ phenomena. With the help of monitoring result, the PQ engineers may adopt proper control strategies. In this paper, the discrete wavelet transform is used to extract the features of transients caused by the load/capacitor switching. The wavelet coefficients are then served as inputs to the hybrid self-organizing mapping neural network for detecting/identifying switching types and phase angles. The simulation results obtained from a distribution system show the applicability of the proposed method.

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

Self-Organizing Map Based Approach for Assessment of Control Room Operator Training

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

This study was performed to examine the mechanism of flow-induced vibration (FIV) in a hard disk drive (HDD). For this purpose, data mining using self-organizing map (SOM) and Bayesian network was applied to unsteady computational fluid dynamics (CFD) simulation data for a hard disk drive. The present data mining started from the extraction of temporal indices from the time series data of fluid properties given at each grid point. Then, a set of grid points was divided into several clusters based on the similarity of the temporal indices by using SOM, and the clustered data were mapped onto a real space of HDD. Through this process, characteristic phenomena latent in the unsteady flow field were classified and identified. Finally, the relations between temporal indices and FIV were constructed by using Bayesian network. The resulting network structure revealed a possible mechanism of FIV that originates from a temporal sequence of flow energy dissipation and production.

We need more than words and simple methods to describe the various different color patterns found on printed fabrics nowadays. The complexity in pattern identification has made the analysis and comparison difficult and will have to be managed manually. The automatic computer color separating system for printed fabrics, proposed in this paper, integrates a genetic algorithm (GA) and a self-organizing map network (SOMN) to automatically separate printed colors, so as to eliminate the time-consuming manual color segmentation and registration currently done in the industry. The system first uses a color scanner to record RGB color images of the printed fabrics and uses median filter processing to reduce color changes due to uneven light reflections arising from the fabric surface weaving texture. Then RGB color space is transformed to HSI color space so that color analysis can match human color sense and use customary procedures. Next, the GA is employed to search for color distributions that are the same as the original image of printed fabrics. The area of each sub-image is 9.06% of the original image, not only reducing color segmentation operation time, but completely reserving the print structure and color distribution of the original image. Afterwards, color characteristic values are obtained in HSI color space. Finally, SOMN is adopted for the color segmentation operation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns in images from printed fabrics.

Video compression is an essential task in video storage and transmission. In this paper, we worked for compressing the video sequence using Self-organizing map (SOM) and then stored the output feature vector of SOM using Hopfield Neural Network. The SOM algorithm is based on unsupervised, competitive learning. It extracts the useful features from video frames by feature extraction method. In our approach, the video sequence is first divided into video frames. The frames are processed by passing to the input of Self-organizing map (SOM) that outputs the informative features by removing some of the redundant information contained in video frames. Self-organizing map is an efficient method for reducing the size of images in the neural network field. The feature vector obtained is further used for storing the patterns using Hopfield network. The feature vector is applied to the input of the Hopfield neural network and stored in the network for encoding the video frames. Thus the compressed video frames are encoded and stored in memory as a codeword. The codeword is used for reconstruction of the video frames by using the same Hopfield network. The codeword is applied to the Self-organizing map network and the video frames are reconstructed from those codeword. Simulation results show that high compression rate is achieved while maintaining the good reconstruction quality of images.

The objective of this work is to develop a digitized mammogram feature extraction approach using Kohonen's self-organizing maps (SOM). Once developed, the SOM network can be used as the first processing stage in a breast cancer computer aided diagnosis system. Its role is to offer segmented data as input to a second stage dedicated to the diagnosis task, which is implemented via a multilayer perceptron trained by the backpropagation algorithm.