A Hierarchical Extended Kohonen Map (HEKM) learns to associate actions to perceptions under the supervision of a planner: they cooperate to solve path nding problems. We argue for the utility of using the hierarchical version of the KM instead of the \\at" KM. We measure the beneets of cooperative learning due to the interaction of neighboring neurons in the HEKM. We highlight a beneecial side-...

The Kohonen Algorithm was extended by (a) a coupling between node and input space based upon gravitational principles and (b) a controling mechanism based upon two-point correlation functions. The extended algorithm avoids optimization of the learning rate and neighbourhood parameters. Applications are given for both a 3-dimensional example data set with mixed topologies and a 13-dimensional da...

This paper presents a probabilistic unsupervised neural method in order to construct the lithofacies of the wells HM2 and HM3 situated in the south of Algeria (Sahara). Our objective is to facilitate the experts' work in geological domain and to allow them to obtain the structure and the nature of lands around the drilling quickly. For this, we propose the use of the Self-Organized Map (SOM) of...

A chaotic self-organizing map can be produced by replacing the linear neural units of the conventional selforganizing map with neural units capable of producing chaos. The introduction of chaos into the selforganizing map is shown to improve the ability of the network to cluster input patterns. output converges to a single non-zero value. As g is increased beyond 0.75 the output begins to oscil...

The Self-Organizing Map (SOM) is a powerful neural network method for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional measurement data into simple geometric relationships, usually on a two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data eleme...