Sitao Wu

Induction machine fault detection using SOM-based RBF neural networks

A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.

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.

PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map

Self-organizing map (SOM) is an approach of nonlinear dimension reduction and can be used for visualization. It only preserves topological structures of input data on the projected output space. The interneuron distances of SOM are not preserved from input space into output space such that the visualization of SOM can be degraded. Visualization-induced SOM (ViSOM) has been proposed to overcome this problem. However, ViSOM is derived from heuristic and no cost function is assigned to it. In this paper, a probabilistic regularized SOM (PRSOM) is proposed to give a better visualization effect. It is associated with a cost function and gives a principled rule for weight-updating. The advantages of both multidimensional scaling (MDS) and SOM are incorporated in PRSOM. Like MDS, The interneuron distances of PRSOM in input space resemble those in output space, which are predefined before training. Instead of the hard assignment by ViSOM, the soft assignment by PRSOM can be further utilized to enhance the visualization effect. Experimental results demonstrate the effectiveness of the proposed PRSOM method compared with other dimension reduction methods.

Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization

A self-organizing and self-evolving agents (SOSENs) neural network is proposed. Each neuron of the SOSENs evolves itself with a simulated annealing (SA) algorithm. The self-evolving behavior of each neuron is a local improvement that results in speeding up the convergence. The chance of reaching the global optimum is increased because multiple SAs are run in a searching space. Optimum results obtained by the SOSENs are better in average than those obtained by a single SA. Experimental results show that the SOSENs have less temperature changes than the SA to reach the global minimum. Every neuron exhibits a self-organizing behavior, which is similar to those of the self-organizing map (SOM), particle swarm optimization (PSO), and self-organizing migrating algorithm (SOMA). At last, the computational time of parallel SOSENs can be less than the SA

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.

A flexible multi-layer self-organizing map for generic processing of tree-structured data

A new multi-layer self-organizing map (MLSOM) is proposed for unsupervised processing tree-structured data. The MLSOM is an improved self-organizing map for handling structured data. By introducing multiple SOM layers, the MLSOM can overcome the computational speed and visualization problems of SOM for structured data (SOM-SD). Node data in different levels of a tree are processed in different layers of the MLSOM. Root nodes are dedicatedly processed on the top SOM layer enabling the MLSOM a better utilization of SOM map compared with the SOM-SD. Thus, the MLSOM exhibits better data organization, clustering, visualization, and classification results of tree-structured data. Experimental results on three different data sets demonstrate that the proposed MLSOM approach can be more efficient and effective than the SOM-SD.

Cell-splitting grid: a self-creating and self-organizing neural network

Abstract A new model of self-creating and self-organizing neural network, cell-splitting grid (CSG), is presented. In this proposed CSG algorithm, the neurons and their connections are created and organized on a 2-D plane according to the input data distribution. Compared with self-organizing map (SOM) and its other improved algorithms, the CSG algorithm has outperformed SOM and other SOM-related algorithms in vector quantization while maintaining relatively good topology preservation. This paper shows that CSG is a promising and an effective method especially for non-uniformly distributed data.

An online cellular probabilistic self-organizing map for static and dynamic data sets

In this paper, a new online cellular probabilistic self-organizing map (CPSOM) is presented. The proposed online CPSOM is derived from the batch mode soft topological vector quantization (STVQ). It requires less storage than the STVQ such that it is able to deal with much larger data sets. It converges faster than the STVQ with the same effect when the map size is relatively small, and forms more ordered topology than the STVQ when the map size is relatively large. Most of all, by tuning a parameter in the CPSOM as a forgetting factor, the CPSOM can be used not only in static data sets, but also in dynamic data sets, where the input data come in endlessly and dynamically. The online CPSOM provides more information about the assignment probability for each neuron, which proved to be very useful for unsupervised clustering of the CPSOM.

Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index

Classical clustering methods, such as partitioning and hierarchical clustering algorithms, often fail to deliver satisfactory results, given clusters of arbitrary shapes. Motivated by a clustering validity index based on inter-cluster and intra-cluster density, we propose that the clustering validity index be used not only globally to find optimal partitions of input data, but also locally to determine which two neighboring clusters are to be merged in a hierarchical clustering of Self-Organizing Map (SOM). A new two-level SOM-based clustering algorithm using the clustering validity index is also proposed. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical clustering algorithms on an SOM.

Improvement of borrowing channel assignment for patterned traffic load by online cellular probabilistic self-organizing map

This paper describes an improvement of borrowing channel assignment (BCA) for patterned traffic load by using the short-term traffic prediction ability of cellular probabilistic self-organizing map (CPSOM). The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques today. In case of patterned traffic load, the traditional BCA methods are not efficient to further enhance the performance because heavy-traffic cells cannot borrow channels from their neighboring cells with light or medium traffic that may have unused nominal channels. The performance can be increased if the short-term traffic load can be predicted. The predicted results can then be used for channel re-assignment. Therefore, the unused nominal channels of the light-or-medium-traffic cells can be transferred to the heavy-traffic cells that need more nominal channels. In this paper, CPSOM is used online for traffic prediction. In this sense, the proposed CPSOM-based BCA method is able to enhance the performance for patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method enables the system to work with better performance for patterned traffic load than the traditional BCA methods.