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Continuous automaton
About: Continuous automaton is a research topic. Over the lifetime, 947 publications have been published within this topic receiving 17417 citations.
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TL;DR: Through enumerating all possible two-state and three-color IFAs, this paper shows that there are a variety of complex behaviors in these simple computational systems.
Abstract: The iterated finite automaton (IFA) was invented by Stephen Wolfram for studying the conventional finite state automaton (FSA) by means of A New Kind of Science methodology. An IFA is a composition of an FSA and a tape with limited cells. The complexity of behaviors generated by various FSAs operating on different tapes can be visualized by two-dimensional patterns. Through enumerating all possible two-state and three-color IFAs, this paper shows that there are a variety of complex behaviors in these simple computational systems. These patterns can be divided into eight classes such as regular patterns, noisy structures, complex behaviors, and so forth. Also they show the similarity between IFAs and elementary cellular automata. Furthermore, any cellular automaton can be emulated by an IFA and vice versa. That means IFAs support universal computation.
2 citations
2 citations
18 Jul 2010
TL;DR: To provide analytical proof for stability and other key properties of a fuzzy controller a Takagi-Sugeno (TS) model is preferred and in this paper a TS-type fuzzy automaton is introduced.
Abstract: Tracking the status of an event-driven, large control system is a difficult problem. Those systems often encounter unexpected events in an uncertain environment. Using a fuzzy automaton offers an effective approximation method to model continuous and discrete signals in a single theoretical framework. A Max-Min automaton can successfully model a cluster of relevant states when a decision is to be made on the next state of a goal path at the supervisory level. However, to provide analytical proof for stability and other key properties of a fuzzy controller a Takagi-Sugeno (TS) model is preferred. In this paper a TS-type fuzzy automaton is introduced.
2 citations
17 Nov 1988
TL;DR: In this lecture the map from a cellular automaton to a sequence of analytical approximations called the local structure theory is described and it is suggested that the process by which a cellular Automaton holds particular probability measures invariant is an appropriate model for biological memory.
Abstract: In this lecture the map from a cellular automaton to a sequence of analytical approximations called the local structure theory is described Connections are drawn between cellular automata and neural network models It is suggested that the process by which a cellular automaton holds particular probability measures invariant is an appropriate model for biological memory 20 figs
2 citations
07 Jul 2013
TL;DR: The method includes a homogeneous fuzzy cellular automaton whose representative cell is a Takagi-Sugeno-Kang fuzzy system that is tuned by means of the differential evolution algorithm to optimize a similarity measure between the dynamics of the target complex system and the automaton.
Abstract: This paper presents a proposal to model the dynamics of complex systems that can be represented as homogeneous continuous cellular automata. The method includes a homogeneous fuzzy cellular automaton whose representative cell is a Takagi-Sugeno-Kang fuzzy system. This cell is tuned by means of the differential evolution algorithm, which tries to optimize a similarity measure between the dynamics of the target complex system and the automaton. The experiments show that our approach is able to obtain a valid model for a complex system that exhibits a two-dimensional nonlinear wave scheme.
2 citations