The purpose of this work is to investigate a hybrid approach (neuro-immune technique) for anomaly detection on time series data. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need both positive and negative samples. The proposed approach uses normal samples to generate abnormal samples t...

We present a new neural network algorithm, derived from the Kohonen self-organized mapping algorithm, for the solution of the problem of matching points in two pictures representing slightly displaced and distorted images of the same objects. We describe it hereafter in the context of a particular application, namely the matching of the images of marker-particles suspended in a moving fluid, se...

This paper proposes a method to detect network intrusions by using the PCASOM (principal components analysis and self-organizing map) neural networks. A modified unsupervised learning algorithm which is more suitable for intrusion detection is presented. Experiments are carried out to illustrate the performance of the proposed method by using DARPA 1998 evaluation data sets. It shows that the p...

We address the problem of Topic Detection and Tracking (TDT) and subsequently detecting trends from a stream of text documents. Formulating TDT as a clustering problem in a class of self-organizing neural networks, called the Adaptive Resonance Theory (ART) networks, we propose an incremental algorithm to solve this clustering problem. From the topics being detected and tracked, we show how tre...

Abstract--In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining relation-based neurofuzzy networks (NFN) and self-organizing polynomial neural networks (PNN). For such networks we develop a comprehensive design methodology and carry out a series of numeric experiments using data coming from the area of software engineering...

In this paper, neuro based intelligent diagnosis methods for electro-mechanical control system are proposed. A self organizing map neural network (SOM) is used to classify measured data of the target system as a qualitative diagnostic method. Besides of the above procedure, it is expected to attain more efficient maintenance by a quantitative estimation of failure. For the purpose, new method i...

A dynamic modeling of a robot manipulator by means of a neural architecture is presented. Such model is applicable to generate a decoupling and linearizing feedback in the control loop of the robot drives. A modified extended Self-Organizing Map is used to perform the needed mapping of the robot’s movement state to the according joint torques. To get improved control performance by this, both c...

Self-Organizing Maps, or Kohonen networks, are a widely used neural network architecture. This paper starts with a brief overview of how selforganizing maps can be used in different types of problems. A simple and intuitive explanation of how a self-organizing map is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed...