5,615 research outputs found
Aspects of fuzzy control and estimation
No full textThis paper gives an overview of Fuzzy Systems for application to fuzzy modelling, analysis, control and estimation. This first section describes the major concepts together with future research objectives without requiring any a priori knowledge on the part of the reader. Further sections introduce the mathematical notation required to describe fuzzy systems and review existing application and research areas. The subject of performance analysis tools for fuzzy systems is highlighted as a current research area and an example of a simple analysis tool is given. The topic of signal estimation using fuzzy models is also discussed and a target tracking example is included. Comparisons are also drawn between fuzzy systems and single layer associative memory neural networks that offer some transparency for dynamical processes modelled as neural networks
Phase plane analysis tools for a class of fuzzy control systems
No full textAlthough fuzzy controllers have been fully applied with success, one of the reasons they are not used more generally could be the lack of analysis tools. This paper describes a performance prediction and design tool, applicable to a class of systems that have quasi second order behaviour, which is analogous to the algebraic phase plane approach. Using this technique the response of a rule based system can be investigated and the influence of individual ruls on overall performance can be determined, allowing a stability analysis to be carried out directly on the rule based system. Implications for controller design are considered. The tools described are supported by a software package written at Southampton University and used by two UK Ministry of Defence establishments for autonomous vehicle control research
Indirect adaptive fuzzy control
No full textFuzzy controllers may be either static systems, which have fixed rule base, or adaptive systems, which have the ability to alter their rules. A discussion of adaptive fuzzy controllers and a comparison with corresponding algebraic techniques concludes that all previous adaptive fuzzy controllers have been of the direct adaptive type. Such controllers use observations of closed loop control performance to manipulate the controller rule base directly without any intermediate process model being produced. In this paper, an indirect adaptive fuzzy controller is proposed where an intermediate process model, identified for observed data, is used to peform on-line controller design. The resulting separation of the adaptation system from controller design enables learning convergence to be investigated. Examples are given of both fuzzy model identification and controller design for linear and nonlinear processes
How Accretionary Prisms Elucidate Seismogenesis in Subduction Zones
No full textEarthquakes occur along the plate-boundary thrusts underlying accretionary prisms and along out-of-sequence thrusts that cut through prisms. Thermal models suggest that the earthquakes on the plate-boundary thrusts initiate in a temperature range of 125oC to ~350oC. Because syndeformational diagenetic and metamorphic alterations recorded in accretionary prisms have specific temperature ranges, the alterations and the associated deformation can be correlated to the temperature range that accretionary prisms are seismogenic. Comparison of accreted rocks deformed above, within, and below the seismogenic zone suggests characteristics of rocks at seismogenic depths that may make them earthquake prone. During passage through temperatures from 50o to 150oC, accretionary prism sediments become rocks, undergoing diagenetic reactions including the transformation of smectite to illite, albitization of detrital feldspar, dehydration of opal, and the generation of hydrocarbons. Although the smectite to illite transition does not change the frictional properties of the prism so that it becomes seismogenic, water and cations (calcium, magnesium, iron) released during this transition and the albitization process foster cementation. Cementation and veining by carbonates becomes common by 125oC, perhaps due to the above-mentioned release of cations. Pressure solution fabrics begin to be apparent at ~150oC, with well-developed cleavages and quartz veining common by 200oC. Pressure solution may be facilitated by the diagenetic formation of illite. Quartz veining and cementation in the 150o–300oC range facilitates the change from a velocity-strengthening, clay dominated to a velocity-weakening, quartz-influenced, earthquake-prone rheology. The diagenetic-metamorphic reactions occurring at temperatures from 125o to ~300oC cement and add rigidity to the thickening upper plate of the accretionary prism. This developing elastic strength of the upper plate is required to store the elastic strain energy required for an earthquake. In accretionary prisms, brittle fabrics are progressively replaced by ductile fabrics through a temperature range of ~150o– 325oC. Although rocks in the seismogenic zone have lost most of their intergranular fluid through consolidation, vein geometries and fluid inclusions suggest high fluid pressures, approaching lithostatic. Strain localization in the form of discrete shear surfaces occurs across the lower aseismic to seismic transition. Strain localization is observed both at outcrop and map scale. At map scale, the seawardmost occurrence of out-of-sequence thrusts define the leading edge of the rigidified accretionary prism that is capable of storing elastic energy
C.J. Koch (1932 - )
No full textBiographical, bibliographical, and literary historiography of Australian author C.J. Koch
Intelligent Control: Aspects of Fuzzy Logic and Neural Networks
No full textIndex: 1. An Introduction to Intelligent Control 1.1 Preliminaries 1.2 Intelligent Control Requirements and Architectures 1.3 Approaches to Intelligent Control 1.4 Knowledge Based Systems 1.5 Fuzzy Logic 1.6 Fuzzy Logic in Control 1.7 Neurocontrollers 1.8 Higher Level Intelligent Controllers 1.9 Bibliographical Notes 2. Introductory Fuzzy Logic 2.1 Fuzzy Sets and Logic 2.2 Fuzzy Inference and Composition 2.3 Defuzzification 3. Fuzzy Logic Controller Structure and Design 3.1 Introduction 3.2 Applications of Fuzzy Set Theory 3.3 Fuzzy Logic Controller Structural Issues 3.4 Design Requirements of Fuzzy Logic Controllers 4. The Static Fuzzy Logic Controller 4.1 Introduction 4.2 Controller Design by Verbalisation or Expert Interrogation 4.3 The Fuzzy PID Controller 4.4 Parametrically Determined Fuzzy PID Controllers 4.5 Linguistic Rule Inversion Fuzzy Logic Controllers 4.6 Cluster Based Fuzzy Logic Controllers 5. Self-Organising Fuzzy Logic Control 5.1 Introduction 5.2 Control Rule Base SOFLICs 5.3 Rule Based SOFLIC Applications 5.4 Systematic Design of Control Rule Based SOFLIC 6. Indirect Self-Organising Fuzzy Logic Controllers 6.1 Introduction 6.2 Self-Organising Fuzzy Models and Predictors 6.3 Relation Causality Inversion 6.4 Controller Design 6.5 Adaptive Fuzzy Controller 6.6 A Simulation Example of Indirect Adaptive Fuzzy Logic Control 6.7 Nested and Hybrid Fuzzy Controllers 7. Case Studies of Indirect Adaptive Fuzzy Control 7.1 Regulation of a Ship's Heading 7.2 Track Control of a City Bus 7.3 Autonomous Road Vehicle Control and Guidance 7.4 Observations on Indirect Fuzzy Adaptive Control 8. Neural Network Approximation Capability for Control and Modelling 8.1 Introduction 8.2 Approximation Capability of Artificial Neural Networks 8.3 Multilayer Perceptrons in Neurocontrol 8.4 Radial Basis Functions in Modelling and Control 9. The B-spline Neural Network and Fuzzy Logic 9.1 Introduction 9.2 Polynomial Basis Functions 9.3 B-splines for Guidance 9.4 Multivariate Basis Functions 9.5 Weighted Adaptation 9.6 B-spline Neural Net Nonlinear Time Series Predictors and Modelling 9.7 A Comparison between Fuzzy Logic and Single Layer Associative Memory Neural Networks 9.8 Conclusions Appendix: Mathematical Prerequisites A.1 Metric Spaces A.2 Normed Metric Spaces A.3 Algebras A.4 Approximation in Normed Spaces Content
Advances in Intelligent Control
No full textAdvances in Intelligent Control is a collection of essays arranged in two parts. Part one contains recent contributions of artificial neural networks to modelling and control. Part two concerns itself primarily with aspects of fuzzy logic in intelligent control, guidance and estimation although some of the contributions either make direct equivalence relationships to neural networks or use hybrid methods where a neural network is used to develop the fuzzy rule base. Written by an internationally respected team of experts, contents include: Neural networks for modelling and control. Learning control with interpolative memories. Neural network model-based predictive control. Hierarchical fuzzy control. Indirect adaptive fuzzy logic control. Adaptive expert systems. Bibliography. C.J. Harris is Lucas Professor of Aerospace Systems Engineering at the University of Southampton, Southampton, UK. Index: 1. Editor's Introduction - C.J. Harris Part I - Neural Networks in Intelligent Control 2. Neural Networks for Modelling and Control: A Review - M. Brown and C.J. Harris 3. Neural Net Computing and Intelligent Control of Systems - Y.H. Pao et al. 4. Neural Networks for Nonlinear Dynamic System Modelling and Identification - S. Chen and S.A. Billings 5. Learning Control with Interpolative Memories - H. Tolle et al. 6. ASMOD: An algorithm for Adaptive Spline Modelling of Observation Data - T. Kavli 7. Adaptive Control of Nonlinear Systems - F.C. Chen and H.K. Khalil 8. Neural Network Model Based Predictive Control - J. Saint-Donat et al. Part II - Fuzzy Logic Control 9. Aspects of Fuzzy Control and Estimation: A Review - C.G. Moore and C.J. Harris 10. Hierarchical Fuzzy Control - G. Raju 11. Unified Real Time Approximate Reasoning - D. Linkens and J. Nie 12. Indirect Adaptive Fuzzy Logic Control - C.G. Moore and C.J. Harris 13. Adaptive Expert Systems - C. Batur and V. Kasparian 14. Neural Network Based Approximate Reasoning: Principles and Implementation - J. Nie and D. Linkens 15. Self-Organizing Control using Fuzzy Neural Networks - T. Yamaguchi et al. Bibliography Further information can be obtained from: Taylor & Francis Ltd Rankine Road Basingstoke Hampshire RG24 8PR UK or Taylor and Francis Inc. 1900 Frost Road Suite 101 Bristol PA 19007-1598 USA Content
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