Feature extraction and pattern classification of remote sensing data by a modular neural system

Extending the Kohonen self-organizing map networks for clustering analysis

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.

We propose a broad-based target classifier that recognizes a target on the principle of pattern matching based on associative memory, and can be implemented in hardware with standard complementary metal-oxide semiconductor (CMOS) cells. A major drawback in the field of neural networks is the inability to implement network designs in hard- ware inexpensively. Here we have designed an associative RAM-net memory neural classifier, which is based on the associative memory model of a winner-take-all classifier. In this model extensive use of ordi- nary random access memory is made. The benefit of this approach is that the entire architecture can be designed with current CMOS standard cells application-specific integrated circuit (ASIC) technologies as op- posed to current analog very large scale integration (VLSI) approaches. Since RAM can be easily added using the standard cell ASIC approach, a low-cost implementation for a wide variety of neural classification prob- lems is provided. The integration of all necessary features on a single chip results in an easily implementable, low-chip-count classification sys- tem. A full gate-level design of the architecture is created and simulated using the VHDL hardware programming language (Navabi, 1993; Mentor Graphics, 1992). The results are very encouraging, and warrant further research into various memory types and configurations. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)00507-8) Subject terms: target classifier; neural networks; associative memory; RAM-net memory.

A modular neural network architecture for inverse kinematics model learning

Learning soft computing control strategies in a modular neural network architecture

Spiking Self-organizing Maps for Classification Problem

Independent component analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, a novel compound neural network for fault sources recognition was proposed. First, neural ICA algorithm was applied to fusion of multi-channel measurements by sensors. Moreover, further feature extraction was made. Thus, statistical features higher than second order were captured from the measurements. Second, a typical neural classifier such as the back-propagation (BP), the radial basis function (RBF) or the SOM network was trained for the final fault sources recognition. The results from contrast experiments in fault diagnosis of rotating machines show that the proposed compound neural network with ICA based feature extraction can recognize various fault sources at considerable accuracy, and be constructed in simpler way, both of which imply its great potential in fault diagnosis.

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.

Identification of lithofacies using Kohonen self-organizing maps

Describes a series of experiments on using Kohonen self-organizing maps and hybrid systems for continuous speech recognition. Experiments with different nonlinear transformations on the signal before using a neural network has been done and results compared. The hybrid system developed by the authors combines self-organizing feature maps with dynamic time warping. The experiments suggest that the combination has better performance than either of the two methods applied individually. >

An investigation of local energy surface detection integrated with neural network techniques for image segmentation is presented, as applied in the feature extraction of chromosomes from image datasets obtained using an experimental confocal microscope. Use of the confocal microscope enables biologists to observe dividing cells (living or preserved) within a three-dimensional (3-D) volume, that can be visualised from multiple aspects, allowing for increased structural insight. The Nomarski differential interference contrast mode used for imaging translucent specimens, such as chromosomes, produces images not suitable for volume rendering. Segmentation of the chromosomes from this data is, thus, necessary. A neural network based on competitive learning, known as Kohonen's self-organizing feature map (SOFM) was used to perform segmentation, using a collection of statistics or features defining the image. Our past investigation showed that standard features such as the localized mean and variance of pixel intensities provided reasonable extraction of objects such as mitotic chromosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigated as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is applied in a SOFM, producing 3-D images exhibiting vast improvement in the level of detail and clearly isolating the chromosomes from the background. Index Terms-DIC, differential interference contrast, feature extraction, feature space, image segmentation, local energy, Morlet wavelet, phase congruency, self organizing feature map, SOFM.

The paper compares two modular neural network architectures, built up of adaptive resonance theory (ART) networks, that can develop stable two-level hierarchical clusterings of arbitrary sequences of binary input patterns. In particular, it contrasts the typical class hierarchies that the networks found on a machine learning benchmark database. It is proposed that the main difference between the two clusterings are the direct consequence of the existence or absence of an internal feedback mechanism and explicit associative links between a higher-level class and its sub-classes.

We have proposed a constructive learning method, called multi-sieving learning, for implementing automatic decomposition of learning tasks and a parallel and modular multi-sieving network architecture in our previous work (1995). In this paper we present a new parallel and modular multi-sieving neural network architecture to which multiple control networks are introduced. In this architecture the learning task for a control network is decomposed into a finite set of manageable subtasks, and each subtask is learned by an individual control sub-network. An important advantage of this architecture is that the learning tasks for control networks can be learned efficiently, and therefore automatic decomposition of complex learning tasks can be achieved easily.

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

The article presents neural network for recognition of driving strategies based on interactions between drivers in road traffic. It analyzes the architecture of the model implemented as a self-organizing map (SOM), consisting of a group of neural networks based on radial basis functions (RBF). It is a training model grounded in the biological foundations of artificial neural networks, in which the training set should consist exclusively of input vectors; wherein the network training algorithm adjusts itself the network's weights to obtain consistent output vectors (i.e. to make presenting sufficiently close input vectors result into the same outputs).\nThe article presents the results of using a new generation of the neural network developed by us, which includes an adaptive learning algorithm to reduce the effect of re-training (overfitting) and false recognition, as well as to improve the determination of the boundaries between clusters.\nThe aim of the research is to outline architecture and structure of the neural network model that allows recognizing strategical characteristics of driving and can identify strategies of interactions between vehicles (their drivers) in road traffic as well as identify behavioral patterns This paper considers driving strategies that characterize the interaction of dyads of vehicles (drivers) moving in road traffic. The research results show that the SOM RBF neural networks can recognize and classify types of interactions in road traffic based on modelling of the analysis of vehicle movement trajectories. Experimental results demonstrate the neural networks architecture and networks learning involving 400 iterations of streaming the training data representing 500 possible simulated interaction situations. This paper presents a novel neural network model for recognition of drivers' behaviour patterns and for classification of driving strategies into five general classes: (1) competition strategy, (2) contest strategy, (3) evasion strategy, (4) compromise strategy, and (5) active confrontation strategy. This neural network has demonstrated a high rate of recognition and concise clusterization of similar driving strategies. The key contribution of this paper: it proposes a neural network model based on Kohonen's Self-Organizing Map (SOM) for detecting drivers' behaviours from vehicle movement patterns - driving strategies - instead of monitoring driver's specific activities.