2016/06/14 by Frieder Stolzenburg, Stolzenburg, Frieder, Florian Ruh +1
Computer Science · #FOS: Computer and information sciences #Fuzzy Logic and Control Systems #Machine Learning and Algorithms #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.1606.04466
16 pages, 10 figures. This paper is an extended version of a contribution presented at KI 2009 Workshop Complex Cognition
arxiv created 2016/06/14 · openalex publication_date 2016/06/14 · arxiv updated 2016/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The fields of neural computation and artificial neural networks have developed much in the last decades. Most of the works in these fields focus on implementing and/or learning discrete functions or behavior. However, technical, physical, and also cognitive processes evolve continuously in time. This cannot be described directly with standard architectures of artificial neural networks such as multi-layer feed-forward perceptrons. Therefore, in this paper, we will argue that neural networks modeling continuous time are needed explicitly for this purpose, because with them the synthesis and analysis of continuous and possibly periodic processes in time are possible (e.g. for robot behavior) besides computing discrete classification functions (e.g. for logical reasoning). We will relate possible neural network architectures with (hybrid) automata models that allow to express continuous processes.