R68-40 Sequential Machines and Automata Theory
نویسنده
چکیده
منابع مشابه
The Full-decomposition of Sequential Machines with the Separate Realization of the Next-state and Output Functions
The decomposition theory of sequential machines aims to find answers to the following important practical problem: how to decompose a complex sequential machine into a number of simpler partial machines in order to: simplify the design, implementation and verification process; make it possible to process (to optimize, to implement, to test, ••. ) the separate partial machines al though it may b...
متن کاملAn Iterative Technique for Determining the Minimal Number of Variables for a Totally Symmetric Function with Repeated Variables
[1] P. Weiner and J. E. Hopcroft, "Modular decomposition of synchronous sequential machines," in Proc. 8th Ann. Symp. Switching and Automata Theory. Oct. 1967. pp. 233-239. [21 , "Bounded fan-in, bounded fan-out uniform decompositions of synchronous sequential machines," Proc. IEEE (Lett.), vol. 56, pp. 1219-1220, July 1968. [31 E. P. Hsieh, C. J. Tan, and M. M. Newborn, "Uniform modular realiz...
متن کاملAutomata on the plane vs particles and collisions
In this note, colorings of the plane by finite sequential machines are compared to previously introduced notions of ultimately periodic tilings of the plane. Finite automata with no counter characterize exactly biperiodic tilings. Finite automata with one counter characterize exactly particles — periodic colorings that are ultimately periodic in every direction. Finite automata with two counter...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملSequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE Trans. Computers
دوره 17 شماره
صفحات -
تاریخ انتشار 1968