Dynamic Cell Structures: Radial Basis Function Networks with Perfect Topology Preservation

Dynamic Cell Structures (DCS) represent a family of artificial neural architectures suited both for unsupervised and supervised learning. They belong to the recently [Martinetz94] introduced class of Topology Representing Networks (TRN) which build perfectly topology preserving feature maps. DCS employ a modified Kohonen learning rule in conjunction with competitive Hebbian learning. The Kohonen type learning rule serves to adjust the synaptic weight vectors while Hebbian learning establishes a dynamic lateral connection structure between the units reflecting the topology of the feature manifold. In case of supervised learning, i.e. function approximation, each neural unit implements a Radial Basis Function, and an additional layer of linear output units adjusts according to a delta-rule. DCS is the first RBF-based approximation scheme attempting to concurrently learn and utilize a perfectly topology preserving map for improved performance. Simulations on a selection of CMU-Benchmarks indicate that the DCS idea applied to the Growing Cell Structure algorithm [Fritzke93b] leads to an efficient and elegant algorithm that can beat conventional models on similar tasks

An Integrated Architecture for Learning of Reactive Behaviors based on Dynamic Cell Structures

In this contribution we want to draw the readers attention to the advantages of controller architectures based on Dynamic Cell Structures (DCS) [5] for learning reactive behaviors of autonomous robots. These include incremental on-line learning, fast output calculation, a flexible integration of different learning rules and a close connection to fuzzy logic. The latter allows for incorporation of prior knowledge and to interpret learning with a DCS as fuzzy rule generation and ad aptation. After successful applications of DCS to tasks involving supervised learning, feedback error learning and incremental category learning, in this article we take reinforcement learning of reactive collision avoidance for an autonomous mobile robot as a further example to demonstrate the validity of our approach. More specifically, we employ a REINFORCE [23] algorithm in combination with an Adaptive Heuristic Critique (AHC) [21] to learn a continuous valued sensory motor mapping for obstacle avoidance with a TRC Labmate from delayed rein forcement. The sensory input consists of eight unprocessed sonar readings, the controller output is the continuous angular and forward velocity of the Labmate. The controller and the AHC are integrated within a single DCS network, and the resulting avoidance behavior of the robot can be analyzed as a set of fuzzy rules, each rule having an additional certainty value

Dynamic Cell Structures for Calibration-free Adaptive Saccade Control of a Four-Degrees-of-Freedom Binocular Head

This paper describes calibration-free adaptive saccade control of a four-degrees- of-freedom binocular head by means of Dynamic Cell Structures (DCS) [2]. Incremental growth of this Radial Basis Function (RBF) based neural network model up to a pre-specified precision results in very small networks suitable for realtime saccade control. By learning and exploiting the topology of the input manifold the controller output calculation is particular fast. Training of the DCS is based on biological inspired error feedback learning and proceeds in two phases. In the first phase we use a crude model of the cameras and the kinematics of the head to learn the topology of the input manifold together with a rough ap proximation of the control function off-line. Different to e.g. Kohonen-type adaptation rules the distribution of neural units is shown to minimize the control error and not to merely mimic the input probability density. In the second phase, the operating phase, the linear output units of the DCS continue to adapt on-line. Besides our TRC binocular head we use a Datacube image processing system and a Staeubli R90 robot arm for automated training in the second phase. The controller is demonstrated to successfully correct errors in the model and to rapidly adapt to changing parameters. The paper also includes a comparison with a conventional (calibrated) inverse kinematics based controller

SPIN-NFDS: Learning and Preset Knowledge for Surface Fusion - A Neural Fuzzy Decision System

this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?". The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy) , and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN-project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment

SPIN-NFDS Learning and Preset Knowledge for Surface Fusion - A Neural Fuzzy Decision System -

The problem to be discussed in this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?" The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy), and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN- project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment