Property Of Self-organizing Map Research Articles

Self-Organizing Map-Based Resilience Quantification and Resilient Control of Distribution Systems Under Extreme Events

Due to climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution system operators (DSO) to ensure that there is uninterrupted power supply to critical loads in their networks. To embed resilience into DSO’s decision-making, resilience needs to be first quantified and then integrated into the system-level optimization. Therefore, this paper first develops a novel self-organizing map (SOM) based method (called SomRes) to quantify the time-varying resilience index of a system that can leverage the powerful classification property of SOMs and removes some of the disadvantages of subjective weight assignment methods. Using SomRes, a resilient resource allocation and operational dispatch algorithm is further developed to enhance system resilience against extreme events by considering the SomRes resilience index directly as the feedback. The proposed resilience quantification approach is benchmarked with a state-of-the-art approach and the efficacy of the proposed resilient dispatch algorithm is demonstrated through several deterministic and statistical case studies on the IEEE 123-bus distribution system. Simulation studies show that the proposed SomRes quantification method is an appropriate indicator of system resilience, and the resilient resource allocation and dispatch strategy can significantly reduce critical load shedding under varying event propagation scenarios.

A spiking self-organizing map combining STDP, oscillations, and continuous learning.

The self-organizing map (SOM) is a neural network algorithm to create topographically ordered spatial representations of an input data set using unsupervised learning. The SOM algorithm is inspired by the feature maps found in mammalian cortices but lacks some important functional properties of its biological equivalents. Neurons have no direct access to global information, transmit information through spikes and may be using phasic coding of spike times within synchronized oscillations, receive continuous input from the environment, do not necessarily alter network properties such as learning rate and lateral connectivity throughout training, and learn through relative timing of action potentials across a synaptic connection. In this paper, a network of integrate-and-fire neurons is presented that incorporates solutions to each of these issues through the neuron model and network structure. Results of the simulated experiments assessing map formation using artificial data as well as the Iris and Wisconsin Breast Cancer datasets show that this novel implementation maintains fundamental properties of the conventional SOM, thereby representing a significant step toward further understanding of the self-organizational properties of the brain while providing an additional method for implementing SOMs that can be utilized for future modeling in software or special purpose spiking neuron hardware.

On the Use of Kohonen Neural Networks for Site Effects Assessment by Means of H/V Weak-Motion Spectral Ratio: Application in Rio-Antirrio (Greece)

Abstract The investigated area, located around the Rio-Antirrio Strait, Central Greece, has been the target of a seismic microzonation campaign. Seventy seismic stations have been deployed for a period of 4 months, recording in continuous mode. Despite the high level of urban noise, we compiled a data set of 95 earthquakes recorded at most of the 70 sites. By employing the attributes of self-organizing maps (SOMs), a quality-control and signal-improving method is proposed. A SOM (Kohonen, 1997) is a type of unsupervised neural network. The main property of SOMs utilized is that while the competitive learning algorithm on whom this method is based maps the input data on an n -dimensional grid of neurons, the topological relations (proximity of patterns in input data) are preserved in the output space. SOM is applied to the horizontal-to-vertical spectral ratios (HVSR) of every weak event analyzed for each station separately and allows a better evaluation of the stability of the HVSR.

Effects of Irregular Topology in Spherical Self-Organizing Maps

The authors explore the effect of different topologies on properties of self-organizing maps (SOM). They suggest several diagnostics for measuring topology-induced errors in SOM and use these in a comparison of four different topologies. The results show that SOM is less sensitive to localized irregularities in the network structure than the literature may otherwise suggest. Further, the results support the use of spherical topologies as a solution to the boundary problem in traditional SOM.

Fast codebook searching in a SOM-based vector quantizer for image compression

We propose a novel method for fast codebook searching in self-organizing map (SOM)-generated codebooks. This method performs a non-exhaustive search of the codebook to find a good match for an input vector. While performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. This aspect of SOM remained largely unexploited till date. In this paper we first develop two separate strategies for fast codebook searching by exploiting the properties of SOM and then combine these strategies to develop the proposed method for improved overall performance. Though the method is general enough to be applied for any kind of signal domain, in the present paper we demonstrate its efficacy with spatial vector quantization of gray-scale images.

Segmentação de mapas auto-organizáveis com espaço de saída 3-D

O mapa de Kohonen (SOM) tem sido utilizado como ferramenta para visualização de dados de elevada dimensionalidade. Características importantes da rede SOM incluem a compressão de informação e a tentativa de manutenção da topologia dos dados. Dados similares no espaço de entrada deveriam ser mapeados no mesmo neurônio, ou em neurônios vizinhos. Uma das ferramentas de visualização de um mapa 2-D treinado é U-matrix, que apresenta as relações de distância de pesos entre neurônios vizinhos do espaço de saída. A suposição de preservação topológica não é verdade em muitos problemas envolvendo redução de dimensionalidade. Com a automação da detecção de agrupamentos na rede SOM espaços de saída maiores podem ser utilizados em problemas envolvendo a descoberta de classes em dados multidimensionais. Mostra-se a ocorrência de erros topológicos em um exemplo simples de agrupamentos de dados 2-D em um mapa com saída 1-D. Este artigo apresenta uma extensão da U-matrix, o U-array, para espaços de saída maior que 2-D e sua aplicação em conjunto com o algoritmo SL-SOM, que possibilita a detecção do número e o geometria das classes em mapas treinados. Todo o processo é não-supervisionado. A vantagem de trabalhar com dimensões mais elevadas no espaço de saída é a melhor preservação da topologia em problemas de análise automática de dados. Apresenta-se um exemplo de uso de descoberta de classes de dados não linearmente separáveis.

Monitoring of Distillation Column Operation Through Self-Organizing Maps

Monitoring of chemical processes is becoming increasingly difticult as a result of growing complexity and larger scale of operation. In this paper, Kohonen self-organizing map (SOM) is used to monitor the operation of a lab-scale distillation column and to identify process states. SOM projects high-dimensional data to a lower two dimensional grid map while preserving the metric relations of the original data. The results from this paper show that using this property of SOM, process monitoring can be performed effectively through observing time series trajectory of process operations on SOM while fulfilling the objective of state identification at the same time. Occurrence of a fault will result in the deviation from the normal operating trajectory. Root cause identification can also be performed through simple visualization of component planes.

Verifying the Proximity and Size Hypothesis for Self-Organizing Maps

:The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. This article presents research in which we sought to validate these properties of SOM, called the Proximity and Size Hypotheses, through a user evaluation study. Building upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. We also demonstrated a positive relationship between the size of an SOM region and the number of documents contained in the region. We believe this research has established the Kohonen SOM algorithm as an intuitively appealing and promising neural-network-based textual classification technique for addressing part of the longstanding “information overload” problem.