scispace - formally typeset
JournalISSN: 0018-9472

IEEE Transactions on Systems, Man, and Cybernetics 

About: IEEE Transactions on Systems, Man, and Cybernetics is an academic journal. The journal publishes majorly in the area(s): Control theory & Nonlinear system. It has an ISSN identifier of 0018-9472. Over the lifetime, 6054 publication(s) have been published receiving 250977 citation(s).


Papers
More filters
Journal Article
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
Abstract: Estimation theory has had a tremendous impact on many problem areas over the past two decades. Beginning with its original use in the aerospace industry, its applications can now be found in many different areas such as control and communjcations, power systems, transportation systems, bioengineering, image processing, etc. Along with linear system theory and optimal control, a course in estimation theorycan be found in the graduate system and control curriculum,of most schools in the country. In fact, it is probably one of the most,salable courses as far as employment is concerned. However, despite its economic value and the amount of activities in the field, very few books on estimation theory have appeared recently. This book helps to fill the void in the market and does that in a superb manner. Although the book is called OptimalFiltering, the coverage is restricted to discrete time filtering. A more appropriate title would thus be Optimal Discrete Time ,Filtering. The authors’ decision to concentrate on discrete time f lters is due to “recent technological developments as well as the easier path offered students and instructors.” This is probably a wise move since a thorough treatment of continuous time filtering will require a better knowledge o f stochastic processes than most graduate students or engineers will have. As it stands now, the text requires little background beyond that of linear system theory and probability theory. Written by active researchers, in the area, the book covers the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation. Much of the material in the book has been around for a long time and has been widely used, by practitioners in the area: Some results are more recent. However,-it .has been difficult to locate all of them presented in a n organized manner within a single text. This is especially true of the chapters dealing with the computation aspects and nonlinear and adaptive estimation. After a short introductory chapter, Chapter 2 introduces the mathematical model to be used throughout most of the book. The discrete time Kalman filter is 1 hen presented in Chapter 3, along with some applications. Chapter 4 contains a treatment

4,836 citations

Journal Article
TL;DR: The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy.
Abstract: During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory. Fuzzy control is based on fuzzy logic. The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. A survey of the FLC is presented; a general methodology for constructing an FLC and assessing its performance is described; and problems that need further research are pointed out

4,823 citations

Journal Article
TL;DR: A vague set is a set of objects, each of which has a grade of membership whose value is a continuous subinterval of according to the inequality of the following type:
Abstract: A vague set is a set of objects, each of which has a grade of membership whose value is a continuous subinterval of

1,452 citations

Network Information
Related Journals (5)
IEEE Transactions on Neural Networks
6.7K papers, 522K citations
93% related
Neurocomputing
16.5K papers, 389.6K citations
91% related
Information Sciences
13.4K papers, 452.7K citations
88% related
IEEE Access
54.7K papers, 518.1K citations
85% related
Neural Networks
4.8K papers, 312.1K citations
84% related
Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
20211,645
20201,440
2019611
2018487
2017644
2016283