temporarily out of stock
ARTIFICIAL INTELLIGENCE IN REAL TIME CONTROL 1998 IPV
Y.H. Pao, S.R. LeClair
This symposium was the seventh in a very successful series in this field. Since the beginning of the series, there have been a number of very positive developments in the topical area of 'Intelligent Control'. In particular, the area referred to as 'situated control' has stimulated the formation of new perspectives towards real-time intelligent systems. The performances of such artificial species as walking cockroaches, maze-negotiating mice, coke-can collecting robots and the like have encouraged the exploration of yet more adaptive control perspectives.
In this symposium, there was a strong wind of change bringing more consideration of the roles of learning, evolution, hybrid systems and so on under many diverse labels and for many different systems and circumstances.
Chapter headings and selected papers: Architectures for Real-Time Expert Systems. Handling timing in a time-critical reasoning system in a case study (L. Motus). Hybrid Systems. Behavioral programming: enabling a middle-out' approach to learning and intelligent systems (M.S. Branicky). Evolutionary. How fast can a species adapt and still be evolutionary stable (T.L. Vincent). Real-World Tasks. A study and implementation of intelligent node based on LonWorks technology (Junjie Wang). Special Session on Distributed Control & Panel Discussion. Flow control with electric actuators (C.C. Federspiel). Visualization & Imaging. Spectroscopic imaging sensors in materials process control (J.F. Maguire). Evolutionary. Synthetic optimization approach of combining regional guided order principle and biological evolution strategies and its applications (Chang-Yun Shen). Theoretical Issues & Topics. Compactness of a set of membership functions in L2 space and its application to fuzzy optimal control (Takashi Mitsuishi). Fuzzy or Rough Sets. Predictive control by multiple-step linearization of Takagi-Sugeno fuzzy models (S. Mollov et al. ). Framework for approximate time rough control systems an integrated fuzzy sets-rough sets approach (J.F. Peters et al. ). Generic Principles and Strategies. Fault-tolerant process control and some future directions (Jianbo Meng). Nonlinear process modeling using a dynamically recurrent neural network (Shi-Rong Liu). Real-World Tasks. Mimicking a fuzzy flight controller using B-splines (A. Aznar Fernández-Montesinos et al. ).'