✦ LIBER ✦

Control of a deterministic system es a fuzzy environment over infinite planning horizon

✍ Scribed by Janusz Kacprzyk; Piotr Staniewski

Publisher: Elsevier Science
Year: 1983
Tongue: English
Weight: 399 KB
Volume: 10
Category: Article
ISSN: 0165-0114
DOI: 10.1016/s0165-0114(83)80122-5

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A numerical approach to the infinite hor

A numerical approach to the infinite horizon problem of deterministic control theory

✍ M. Falcone 📂 Article 📅 1987 🏛 Springer 🌐 English ⚖ 599 KB

Corrigenda: A numerical approach to the

Corrigenda: A numerical approach to the infinite horizon problem of deterministic control theory

✍ M. Falcone 📂 Article 📅 1991 🏛 Springer 🌐 English ⚖ 50 KB

Comments on “a numerical approach to the

Comments on “a numerical approach to the infinite horizon problem of deterministic control theory”

✍ Mabel Maria Tidball 📂 Article 📅 1991 🏛 Springer 🌐 English ⚖ 94 KB

Multistage control of a stochastic syste

Multistage control of a stochastic system in a fuzzy environment using a genetic algorithm

✍ Janusz Kacprzyk 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 107 KB 👁 2 views

We consider the classic Bellman and Zadeh multistage control problem under fuzzy constraints imposed on applied controls and fuzzy goals imposed on attained states with a stochastic system under control that is assumed to be a Markov chain. An optimal sequence of controls is sought that maximizes th

Output-feedback H∞ control of systems ov

Output-feedback H∞ control of systems over communication networks using a deterministic switching system approach

✍ Dong Kyoo Kim; PooGyeon Park; Jeong Wan Ko 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 319 KB

A neuro-fuzzy system architecture for be

A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments

✍ Wei Li; Chenyu Ma; F.M. Wahl 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 522 KB

A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments is presented. A neural network is used to understand environments. Its inputs are a heading angle between the robot and a specified target, and range information acquired by an array of ultrasonic