Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

Hierarchical control of timed discrete-event systems

An abstract hierarchical control theory is developed for a class of timed discrete-event systems (TDES) within the discrete-event control architectural framework proposed earlier by the authors. For this development, a control theory for TDES is introduced in the spirit of a prior theory of Brandin. A notion of time control structures is introduced, and on its basis a general property of hierarchical consistency is achieved by establishing control consistency — namely preservation of time control structures through the aggregation mapping in a two-level hierarchy.     

Sticker systems with complex structures

 In this paper, we propose a variant of sticker systems which uses molecules with complex structures. Since the original sticker systems (Paun et al. (1998) [2, 8]) working on double strands of DNA have been studied as a formal model for self-assembly in DNA computing, we extend the sticker systems to working on more complex (higher-order) structures of DNA molecules. The advantage of sticker systems with complex structures is that augmented with weak codings we can obtain the characterization of recursively enumerable languages by using only sticking (hybridization) operations for complex molecules, while the usual sticker systems require more complicated operations such as the simultaneous use of couples of dominoes or coherent computations besides morphisms.     

Hierarchical control of discrete-event systems

An abstract hierarchical control theory is developed for discrete-event systems, based on the concepts of control structures and observers. Control structure is an abstract generalization of the family of controllable sublanguages in the Ramadge-Wonham framework. We establish a general version of Zhong's hierarchical consistency by first achieving control consistency — preservation of control structures through the aggregation mapping in a two-level hierarchy. For a refinement of hierarchical consistency with preservation of nonblocking, the concept of observer is introduced via congruences on nondeterministic transition structures.     

Learning classifier systems resources

 This article lists currently available sources of information on classifier systems and classifier systems research, both on-line and in print. The need for new resources, and improvements to certain existing ones, are suggested.     

Soft computing and fuzzy logic

What is soft computing? What is fuzzy computing? What is the relationship between them? This paper intends to provide clear answers to these questions. We focus on exploring the notions of the fuzzy coordinate system and the related transformations between qualitative and quantitative information. These notions are considered to be the core ideas of fuzzy computing. Then the three novel theories of fuzzy computing and soft computing developed by the first author of this paper, namely, the Falling Shadow Representation of fuzzy theory, the Factors Space theory and the Truth Value Flow Inference theory are introduced.     

Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems

In this paper, a new fuzzy adaptive control approach is developed for a class of SISO strict-feedback nonlinear systems, in which the nonlinear functions are unknown and the states are not available for feedback. By fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states. Under the framework of the backstepping design, fuzzy adaptive output feedback control is constructed recursively. It is shown that the proposed fuzzy adaptive control approach guarantees the semi-global boundedness property for all the signals of the resulting closed-loop system. Simulation results are included to illustrate the effectiveness of the proposed techniques.     

Fuzzy hypercubes: Linguistic learning/reasoning systems for intelligent control and identification

This paper introduces a new tool for intelligent control and identification. A robust and reliable learning and reasoning mechanism is addressed based upon fuzzy set theory and fuzzy associative memories. The mechanism storesa priori an initial knowledge base via approximate learning and utilizes this information for identification and control via fuzzy inferencing. This architecture parallels a well-known scheme in which memory implicative rules are stored disjunctively. We call this process afuzzy hypercube. Fuzzy hypercubes can be applied to a class of complex and highly nonlinear systems which suffer from vagueness uncertainty and incomplete information such as fuzziness and ignorance. Evidential aspects of a fuzzy hypercube are treated to assess the degree of certainty or reliability. The implementation issue using fuzzy hypercubes is raised, and finally, a fuzzy hypercube is applied to fuzzy linguistic control.     

Multilevel fuzzy relational systems: structure and identification

 Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.     

基于分层模糊控制的弹性倒立摆系统

随着技术水平的提高,对许多机械系统的高速化和轻量化的要求也变得越来越苛刻,这就需要以减小其重量作为代价,而重量的减少将引起其弹性刚度的减弱,导致系统振动的加剧。为了实现对具有弹性的不稳定系统的控制,该文重点对弹性倒立摆的稳定性控制进行了研究,利用分层模糊控制实现了弹性倒立摆在可移动小车上的动态平衡。通过仿真和具体实验的结果证明,该系统具有良好的鲁棒稳定性。     

Fuzzy evolutionary computing

 This paper elaborates on a new paradigm of computing embracing fuzzy sets and evolutionary methods (specially genetic algorithms). We discuss conceptual and algorithmic enhancements to the individual methods. Fuzzy sets are geared toward granular information processing. Evolutionary computing are population-based optimization methods. In this way, as being components of any hybrid structure, they naturally complement each other. The study reveals a number of representative symbiotic links between fuzzy and genetic computing and provides with relevant illustrative examples.     

直线二级倒立摆模糊算法的应用研究

针对我校智能控制实验室二级倒立摆控制规律单一的现状,对二级倒立摆控制规律进行二次开发.根据二级倒立摆多变量、非线性、强耦合、绝对不稳定的特性.本文采用两种模糊算法来实现二级倒立摆的仿真研究与实时控制.首先,提出了模糊最优控制算法,采用融合函数来解决因多变量的存在而产生的“规则爆炸问题”;其次,提出了分层模糊控制的思想,使系统更符合人类的思维逻辑,更容易控制.     

Soft-computing combining of distributed power control algorithms

In traditional distributed power control (DPC) algorithms, every user in the system is treated in the same way, i.e., the same power control algorithm is applied to every user in the system. In this paper, we divide the users into different groups depending on their channel conditions and use different DPC accordingly. Our motivation comes from the fact that different DPC algorithms have its own advantages and drawbacks, and our aim in this paper is to “combine” the advantages of different DPC algorithms, and we use soft computing techniques for that. In the simulations results, we choose Foschini and Miljanic Algorithm in [3], which has relatively fast convergence but is not robust against time-varying link gain changes and CIR estimation errors, and fixed step algorithm of Kim [3], which is robust but its convergence is slow. By “combining” these two algorithms using soft computing techniques, the resulting algorithm has fast convergence and is robust. Acknowledgments This work was supported in part by GETA (Finnish Academy Graduate School on Electronics, Telecommunications and Automation), Finland.     

Learning rule base in linguistic expert systems

 Linguistic fuzzy control is introduced in a pure logical framework. The problem of learning of the linguistic rule base from the data obtained by monitoring of successful control and the problem of learning the linguistic context are studied. The methods are demonstrated by results of experiments. Supported by the grant No. 201/96/0985 of the GAČR and the project VS96037 of MŠMT of the Czech Republic.     

Crumbling walls: a class of practical and efficient quorum systems

Summary.  A quorum system is a collection of sets (quorums) every two of which intersect. Quorum systems have been used for many applications in the area of distributed systems, including mutual exclusion, data replication and dissemination of information. In this paper we introduce a general class of quorum systems called Crumbling Walls and study its properties. The elements (processors) of a wall are logically arranged in rows of varying widths. A quorum in a wall is the union of one full row and a representative from every row below the full row. This class considerably generalizes a number of known quorum system constructions. The best crumbling wall is the CWlog quorum system. It has small quorums, of size O(lg n), and structural simplicity. The CWlog has optimal availability and optimal load among systems with such small quorum size. It manifests its high quality for all universe sizes, so it is a good choice not only for systems with thousands or millions of processors but also for systems with as few as 3 or 5 processors. Moreover, our analysis shows that the availability will increase and the load will decrease at the optimal rates as the system increases in size. Received: August 1995 / Accepted: August 1996     

Expert supervision and control of a large-scale plant

The process industry and the cement industry in particular, is rapidly realising the importance of expert systems for the control of large-scale production processes which have hitherto defied solution by conventional methods. Where the operational environment of a process industry is subject to vagueness and uncertainty, then expert control offers new opportunities for increased production, fuel economy, and enhanced product quality.This paper outlines a large-scale expert supervision and control system which was developed as part of a long-term project to apply advanced concepts of CIM to a cement production plant. The system comprises a cluster of nine expert subsystems using fuzzy logic, four of which are arranged in a multilayer architecture to synergistically control a rotary kiln and cooler complex. Experience with the real-time expert system since 1985, when it was first commissioned, has resulted in increases of the order of 4–5% in productivity and energy reduction as well as reduced plant maintenance over conventional manual control.     

Fuzzy configuration of matching runtime implementation strategies

 With applications currently growing in complexity and range, increasing numbers of configuration problems are arising in compilers. Already many software systems offer multiple specialized implementation strategies and substrategies, differing in terms of applicability and/or cost, depending on the application context. Configurations then have to be created from the different strategies available in accordance with the application characteristics, the global optimization objective, and potential constraints on the strategies' combinability. In many cases, this results in a combinatorial, i.e., discrete, optimization problem. Proper solutions for automating the configuration while limiting the complexity of the solution search are still being sought. We address here the field of parallel/distributed processing and the configuration of runtime implementation strategies, such as for communication or dynamic load balancing. We present a rule-based approach, integrating fuzzy methodologies for the classification of application characteristics and for gradual selection preference in rules. In this way we exploit available knowledge about the correlation of the problem and solution space, and apply soft computing methods to obtain an approximate, rather than perfect, solution approach, thus helping to limit the configuration complexity. Our approach extends standard fuzzy inference by a multistage organization, and – with proper organization of rules, characteristics and strategies – performs hierarchical fuzzy inference. The approach is demonstrated on concrete configuration examples in parallel compilers.     

Concurrency control in hierarchical multidatabase systems

Over the past decade, significant research has been done towards developing transaction management algorithms for multidatabase systems. Most of this work assumes a monolithic architecture of the multidatabase system with a single software module that follows a single transaction management algorithm to ensure the consistency of data stored in the local databases. This monolithic architecture is not appropriate in a multidatabase environment where the system spans multiple different organizations that are distributed over various geographically distant locations. In this paper, we propose an alternative multidatabase transaction management architecture, where the system is hierarchical in nature. Hierarchical architecture has consequences on the design of transaction management algorithms. An implication of the architecture is that the transaction management algorithms followed by a multidatabase system must be composable– that is, it must be possible to incorporate individual multidatabase systems as elements in a larger multidatabase system. We present a hierarchical architecture for a multidatabase environment and develop techniques for concurrency control in such systems. Edited by R. Sacks-Davis. Received June 27, 1994 / Accepted September 26, 1995     

Fuzzy model based integral action sliding mode control

 A method for combining black box nonlinear models and sliding mode control is presented. In this approach the advantages of the sliding mode control technique are maintained. Parametric uncertainty and unmatched disturbances are acknowledged as limiting factors of controller performance, and their effects are minimised through the use of local linearisation of the nonlinear model. Simulation results demonstrate performance improvements.