Investigation of topographical stability of the concave and convex Self-Organizing Map variant

We investigate, by a systematic numerical study, the parameter dependence of the stability of the Kohonen Self-Organizing Map and the Zheng and Greenleaf concave and convex learning with respect to different input distributions, input and output dimensions

Solving the Inverse Kinematics in Humanoid Robots: A Neural Approach

In this paper a method for solving the inverse kinematics of an humanoid robot based on artificial neural networks is presented. The input of the network is the desired positions and orientations of one foot with respect to the other foot. The output is the joint coordinates that make it possible to reach the goal configuration of the robot leg. To get a good set of sample data to train the neural network the direct kinematics of the robot needs to be developed, so to formulate the relationship between the joint variables and the position and orientation of the robot

Self-organising maps for hierarchical tree view document clustering using contextual information

In this paper we propose an effective method to cluster documents into a dynamically built taxonomy of topics, directly extracted from the documents. We take into account short contextual information within the text corpus, which is weighted by importance and used as input to a set of independently spun growing Self-Organising Maps (SOM). This work shows an increase in precision and labelling quality in the hierarchy of topics, using these indexing units. The use of the tree structure over sets of conventional twodimensional maps creates topic hierarchies that are easy to browse and understand, in which the documents are stored based on their content similarity

A Novel Approach to Gasoline Price Forecasting Based on Karhunen-Loève Transform and Network for Vector Quantization with Voronoid Polyhedral

Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014International audienceWe propose an intelligent approach to gasoline price forecasting as an alternative to the statistical and econometric approaches typically applied in the literature. The linear nature of the statistics and Econometrics models assume normal distribution for input data which makes it unsuitable for forecasting nonlinear, and volatile gasoline price. Karhunen-Loève Transform and Network for Vector Quantization (KLNVQ) is proposed to build a model for the forecasting of gasoline prices. Experimental findings indicated that the proposed KLNVQ outperforms Autoregressive Integrated Moving Average, multiple linear regression, and vector autoregression model. The KLNVQ model constitutes an alternative to the forecasting of gasoline prices and the method has added to methods propose in the literature. Accurate forecasting of gasoline price has implication for the formulation of policies that can help deviate from the hardship of gasoline shortage

Some Theoretical Aspects of the Neural Gas Vector Quantizer

Villmann T, Hammer B, Biehl M. Some theoretical aspects of the neural gas vector quantizer. In: Biehl M, Hammer B, Verleysen M, Villmann T, eds. Similarity Based Clustering. Lecture Notes Artificial Intelligence, 5400. Berlin, Heidelberg: Springer; 2009: 23-34