Dynamic self-organising map

We present in this paper a variation of the self-organising map algorithm where the original time-dependent (learning rate and neighbourhood) learning function is replaced by a time-invariant one. This allows for on-line and continuous learning on both static and dynamic data distributions. One of the property of the newly proposed algorithm is that it does not fit the magnification law and the achieved vector density is not directly proportional to the density of the distribution as found in most vector quantisation algorithms. From a biological point of view, this algorithm sheds light on cortical plasticity seen as a dynamic and tight coupling between the environment and the model.     

On the combination of top-down and bottom-up methodologies for the design of coordination mechanisms in self-organising systems

In resource-flow systems, e.g. production lines, agents are processing resources by applying capabilities to them in a given order. Such systems benefit from self-organization as they become easier to manage and more robust against failures. In this paper, we demonstrate the conception of a decentralized coordination process for resource-flow systems and its integration into an agent-based software system. This process restores a system’s functionality after a failure by propagating information about the error through the system until a fitting agent is found that is able to perform the required function. The mechanism has been designed by combining a top-down design approach for self-organizing resource-flow system and a systemic development framework for the development of decentralized, distributed coordination processes. Using the latter framework, a process is designed and integrated in a system realization that follows the former conceptual model. Evaluations of convergence as well as performance of the mechanism and the required amount of redundancy of the system are performed by simulations.     

New methods for self-organising map visual analysis

Self-organising maps (SOMs) have been used effectively in the visualisation and analysis of multidimensional data, with applications in exploratory data analysis (EDA) and data mining. We present three new techniques for performing visual analysis of SOMs. The first is a computationally light contraction method, closely related to the SOMs training algorithm, designed to facilitate cluster and trajectory analysis. The second is an enhanced geometric interpolation method, related to multidimensional scaling, which forms a mapping from the input space onto the map. Finally, we propose the explicit representation of graphs like the SOMs induced Delaunay triangulation for topology preservation and cluster analysis. The new methods provide an enhanced interpretation of the information contained in an SOM, leading to a better understanding of the data distributions with which they are trained, as well as providing insight into the maps formation.     

Visual feature analysis by the self-organising maps

The Self-Organising Map (SOM) is an Artificial Neural Network (ANN) model consisting of a regular grid of processing units. A model of some multidimensional observation, e.g. a class of digital images, is associated with each unit. The map attempts to represent all the available observations using a restricted set of models. In unsupervised learning, the models become ordered on the grid so that similar models are close to each other. We review here the objective functions and learning rules related to the SOM, starting from vector coding based on a Euclidean metric and extending the theory of arbitrary metrics and to a subspace formalism, in which each SOM unit represents a subspace of the observation space. It is shown that this Adaptive-Subspace SOM (ASSOM) is able to create sets of wavelet- and Gabor-type filters when randomly displaced or moving input patterns are used as training data. No analytical functional form for these filters is thereby postulated. The same kind of adaptive system can create many other kinds of invariant visual filters, like rotation or scale-invariant filters, if there exist corresponding transformations in the training data. The ASSOM system can act as a learning feature-extraction stage for pattern recognisers, being able to adapt to arbitrary sensory environments. We then show that the invariant Gabor features can be effectively used in face recognition, whereby the sets of Gabor filter outputs are coded with the SOM and a face is represented by the histogram over the SOM units.     

Active audition using the parameter-less self-organising map

This paper presents a novel method for enabling a robot to determine the position of a sound source in three dimensions using just two microphones and interaction with its environment. The method uses the Parameter-Less Self-Organising Map (PLSOM) algorithm and Reinforcement Learning (RL) to achieve rapid, accurate response. We also introduce a method for directional filtering using the PLSOM. The presented system is compared to a similar system to evaluate its performance.

Controlling the spread of dynamic self-organising maps

The growing self-organising map (GSOM) has recently been proposed as an alternative neural network architecture based on the traditional self-organising map (SOM). The GSOM provides the user with the ability to control the spread of the map by defining a parameter called the spread factor (SF), which results in enhanced data mining and hierarchical clustering opportunities. When experimenting with the SOM, the grid size (number of rows and columns of nodes) can be changed until a suitable cluster distribution is achieved. In this paper we highlight the effect of the spread factor on the GSOM and contrast this effect with grid size change (increase and decrease) in the SOM. We also present experimental results in support of our claims regarding differences between GSOM and SOM.     

Enhancements to the dynamic self-organising neural network

Class Directed Unsupervised Learning (CDUL) is a dynamic self-organising network which has been shown to overcome many of the problems associated with unsupervised learning, thereby yielding performance characteristics superior to similar networks such as counter-propagation and LVQ. In this paper, the CDUL algorithm is developed further, to a point where the original two-phase learning process is combined into a single system of dynamic parameter variation; a training cycle that can then be terminated automatically at a point of zero error over the training set. The ability to improve training times using a FastCDUL algorithm is also explored. The new algorithm, CDUL2, is subsequently applied to the benchmark problem of mine detection given sonar data, and shown to outperform both backpropagation and LVQ in terms of training speed and recall performance. Finally, a measure of computational cost is estimated for both CDUL2 and LVQ training periods, reinforcing the suggested efficiency of CDUL2 over its counterparts.     

Analysis and application of a self-organising image recognition neural network

This paper investigates the effect of various feature extraction methods on the recognition ability of a self-organising neural network called Paradise when applied to the problems of the classification of face images and hand written character recognition. The feature extraction methods investigated are, oriented Gaussian filters, Gabor filters and oriented Laplacian of Gaussian (LG) filters. The recognition results for the two applications are shown to compare favourably with other techniques designed specifically for the two tasks.     

Texture segmentation using pyramidal Gabor functions and self-organising feature maps

This paper presents texture segmentation realised with image treatment methods and an artificial neural network model. Gabor oriented filters are used to extract frequential texture features and Self-Organising Feature Maps are used to group and interpolate these features. In order to decrease the number of filters, we use a pyramidal multiresolution method of image representation. We intend to build an architecture inspired by the early stages of the visual cortex, while making local frequential analysis of the images, which must be able to segment different textured images.     

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.     

Dynamic coherency identification of generators using self-organising feature maps

An important step in constructing dynamic equivalents of large power systems is the coherency identification and grouping of generators. Self-organising feature maps can do this task, if the attribute vectors, which characterise the features of the generator dynamics inside the network are well chosen. It is shown in the paper that the principal components of the correlation matrix of the simulated time responses of the generators after faults are especially suitable to form the attribute vectors. The results are compared with the use of right eigenvectors and participation factors of the linearised system matrix as attribute vectors.     

BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.     

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound.     

An investigation into affective design using sorting technique and Kohonen self-organising map

In this paper, a prototype system that focuses on affective design perspective for iterative product concept development is proposed and described. The prototype system, which emphasises the solicitation of affective attributes from customers, employs a sorting technique, i.e. picture sorts, for acquiring customer's affective requirements and a hierarchical structure for representing designer's formal elements to meet customer's affective requirements in product conceptualisation. As hierarchical structure alone contains qualitative and uncertain inherence, a self-organised algorithm known as Kohonen self-organising map (SOM) neural network is employed to consolidate the relationship between affective requirements from customers and formal elements from designers so as to formulate a customer-oriented product concept. The performance of the prototype system is illustrated by using a case study on the design of a mobile hand phone.     

A self-organising mixture autoregressive network for FX time series modelling and prediction

Nowadays a great deal of effort has been made in order to gain advantages in foreign exchange (FX) rates predictions. However, most existing techniques seldom excel the simple random walk model in practical applications. This paper describes a self-organising network formed on the basis of a mixture of adaptive autoregressive models. The proposed network, termed self-organising mixture autoregressive (SOMAR) model, can be used to describe and model nonstationary, nonlinear time series by means of a number of underlying local regressive models. An autocorrelation coefficient-based measure is proposed as the similarity measure for assigning input samples to the underlying local models. Experiments on both benchmark time series and several FX rates have been conducted. The results show that the proposed method consistently outperforms other local time series modelling techniques on a range of performance measures including the mean-square-error, correct trend predication percentage, accumulated profit and model variance.     

Parallel self-organising hierarchical neural network based estimation of degree of voltage insecurity

Increased interconnections and loading of power systems, sometimes, lead to insecure operation. Since insecure cases often represent the most severe threats to secure system operation, it is important that the user be provided with a measure for quantifying the severity of the cases both in planning and operational stages of a power system. The Euclidean distance to the closest secure operating point has been used as a measure of the degree of insecurity. Recently, artificial neural networks are proposed increasingly for complex and time-consuming problems of power system. This paper presents a parallel self-organised hierarchical neural network based approach for estimation of the degree of voltage insecurity. Angular distance based clustering is used to select the input features. The proposed method has been tested on IEEE 30-bus system and a practical 75-bus Indian system and found to be suitable for real time implementation in Energy management centre.     

Application of self-organising maps for data mining with incomplete data sets

Self-organising maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.     

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.