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共 223 条记录，以下是181-190 订阅

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systems identification with genetic algorithms

Systems identification with genetic algorithms

引用

iee colloquium on genetic algorithms for control systems engineering

作者： K.J. Hunt Daimler Benz Aerospace Berlin Germany

genetic algorithms are applied to the identification of black-box systems and partially known systems. The approach is best suited to the partially known systems (PKS) problem; in contrast to least-squares-based algorithms for identification of linear black-box systems, corresponding algorithms for identification of partially known systems are in the early stages of development. The best known algorithms for PKS identification suffer from local minima problems. It is shown that the genetic search and optimisation approach overcomes the local minima problem. Further, the approach is applicable immediately to multiparameter PKS identification problems without modification. This paper outlines a framework for black-box and PKS identification with genetic algorithms.< >

关键词： genetic algorithms Identification

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Optimal population size for genetic algorithms: an investigation

Optimal population size for genetic algorithms: an investiga...

引用

iee colloquium on genetic algorithms for control systems engineering

作者： M.O. Odetayo Department of Computing Science De Montfort University Leicester UK

The performance of genetic algorithms (GAs) is affected by the parameters that are employed. In particular, the population size affects the performance and efficiency of GA-based systems. Grefenstette (1986) claimed that a population size between 60-110 is optimal for the convergence of GA-based systems to optimal solution. This paper presents studies that do not support this claim. GAPOLE, a GA-based program, is used to build self-learning self-adaptive self-optimising controllers for a dynamic multi-output unstable system using different population sizes. It is argued that population size may need to be tuned from one application to the other.< >

关键词： Adaptive control Design automation control systems genetic algorithms Optimal control

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genetic-based minimum-time trajectory planning of articulated manipulators with torque constraints

Genetic-based minimum-time trajectory planning of articulate...

引用

iee colloquium on genetic algorithms for control systems engineering

作者： K.K. Chan A.M.S. Zalzala Dept. of Automatic Control & Syst. Eng. Sheffield Univ. UK Robotics Research Group Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper is concerned with introducing a genetic-based algorithm for the minimum-time trajectory planning of articulated robotic manipulators. The planning procedure is performed in the configuration space and respects all physical constraints imposed on the manipulator design, including the limits on the torque values applied to the motor of each joint of the arm; consequently, the complete nonlinear dynamic robot model is incorporated in the formulation.< >

关键词： genetic algorithms Nonlinear systems Motion-planning Robots

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Classifier systems for control

Classifier systems for control

引用

iee colloquium on genetic algorithms for control systems engineering

作者： T.C. Fogarty University of West of England Bristol UK

Classifier systems lie midway between neural networks and symbolic processing systems and potentially combine the benefits of both. They are parallel message-passing rule-based systems which use genetic algorithms to discover new rules as well as providing for reinforcement learning and programming. It has been proposed that a suitable application of genetic algorithms is to evolve robots. A most suitable way to use genetic algorithms to evolve the control systems for robots is within the framework provided by classifier systems. At a SERC workshop on learning systems a number of groups presented successful applications of the genetic algorithm to control problems. However, one cannot evolve complex systems with a simple genetic algorithm nor is it wise or safe to start from scratch in real applications where programmed knowledge can provide constraints for the genetic algorithm to work within. If the genetic algorithm is to be used to evolve control systems for industrial or commercial applications one of the best ways to do this is within the framework of classifier systems.< >

关键词： genetic algorithms Knowledge based systems Pattern recognition Robots

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genetic-based agents for control of distributed systems

Genetic-based agents for control of distributed systems

引用

iee colloquium on genetic algorithms for control systems engineering

作者： T. Clark J.S. Mason Department of Electrical & Electronic Engineering University College of Wales Swansea UK

Management and control of distributed systems are hard tasks for conventional control techniques. Distributed computer control systems (DCCS) and data communication technology have helped to alleviate some of the problems of distributed systems. However, there are some problems that are difficult to solve using conventional methods. Artificial intelligence (AI) techniques have been proposed as solutions to many of the problems inherent in distributed systems. These solutions sometimes prove too complex to use in real systems, and a simpler adaptive system may be needed. This paper discusses adaptive systems from the genetic-based machine learning paradigm, and how they can be integrated with distributed artificial intelligence techniques for the control of distributed systems.< >

关键词： Artificial intelligence Digital control Distributed control genetic algorithms Knowledge based systems

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Generation of collision-free paths, a genetic approach

Generation of collision-free paths, a genetic approach

引用

iee colloquium on genetic algorithms for control systems engineering

作者： J. Solano D.I. Jones Departamento de Electrónica y Automatización IIMAS Universidad Nacional Auténoma de México Mexicali Mexico School of Electronic Engineering and Computer Systems University of Wales Bangor UK

This paper deals with the generation of minimum distance paths for a robot manipulator operating in an environment cluttered with obstacles. These paths are optimised using a genetic approach. The genetic algorithm objective function is formulated in an obstacle avoidance problem context. Evaluation of the genetic algorithm parameters and their behaviour is undertaken in order to determine the most suitable values for this application. Finally cases involving first a mobile robot and then a two-link revolute manipulator are developed.< >

关键词： genetic algorithms Motion-planning Robots

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A distributed genetic algorithm for multivariable fuzzy control

A distributed genetic algorithm for multivariable fuzzy cont...

引用

iee colloquium on genetic algorithms for control systems engineering

作者： D.A. Linkens H. Okola Nyongesa Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK

The traditional approach to multiple parameter optimization in genetic algorithm (GA) practice is to combine the coding of the parameters into a single compound bit-string; the so-called concatenated binary mapping. This approach has some shortcomings; the GA is a competition-based technique that has a natural tendency to evolve one winner which in complex problems yields a solution that is better on some parameters than the others. An extension to the simple GA, called vector evaluated genetic algorithm (VEGA), has been used in multiobjective optimization where one is not interested in a single solution, but a family of optimal solutions. In VEGA each member of the population is evaluated and assigned a weighted fitness value dependent on how it relates to each objective criteria. The reproduction plan then develops groupings within the populations for each of the objectives to be optimized, ensuring that the improvement of one objective does not adversely affect the others. This, however, requires large population sizes and can be quite inefficient. In cases where the complex task is divisible into simpler optimization problems, a better solution set may be obtained using parallel genetic algorithms to search for the optimal solution to each sub-problem.< >

关键词： Fuzzy control genetic algorithms Multivariable systems

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Designing embedded parallel systems with parallel genetic algorithms

Designing embedded parallel systems with parallel genetic al...

引用

iee colloquium on genetic algorithms for control systems engineering

作者： E.-G. Talbi T. Muntean IMAG-Laboratorie de Gonle Informatlque University of Grenoble France

Generic parallel genetic algorithms are developed with reference to the example of the real-time path planning problem for mobile robots. Most robot motion planners are used off-line: the planner is invoked with a model of the environment, it produces a path which is passed to the robot controller which in turn executes it. In general, the time necessary to achieve this loop is not short enough to allow the robot to move in a dynamic environment (moving obstacles). The goal is to try to reduce this time in order to be able to deal with real time path planning in dynamic environments. The authors use a method, called 'Ariadne's CLEW algorithm', to build a global path planner based on the combination of two parallel genetic algorithms: an EXPLORE algorithm and a SEARCH algorithm. The purpose of the EXPLORE algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the SEARCH algorithm is to opportunistically check if the target can be reached from any given placed landmark.< >

关键词： genetic algorithms Mobile robots Parallel algorithms Motion-planning

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The application of genetic algorithms to the optimal selection of parameter values in neural networks for attitude control systems

The application of genetic algorithms to the optimal selecti...

引用

iee colloquium on High Accuracy Platform control in Space

作者： G.W. Game C.D. James Guidance and Control Department British Aerospace Space Systems Limited Bristol UK

Spacecraft attitude control is conventionally achieved by the use of reaction wheel or thruster based control schemes. The authors investigate the use of a neural network controller for a thruster based spacecraft att... 详细信息

关键词： Attitude control genetic algorithms Neural networks Space vehicles

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A DSP soft decision decoder for cyclic error-control codes

A DSP soft decision decoder for cyclic error-control codes

引用

iee colloquium on DSP Applications in Communication systems

作者： J. Eiguren I.I. Dumer P.G. Farrell Department of Electrical Engineering & Electronics University of Manchester Institute of Science and Technology UK

Recent advances in information set decoding techniques for cyclic block codes have made it possible to combine the well-known error trapping decoding technique with an information set decoding technique which splits the received word into two or more sections. The result is a combined splitting and error trapping soft-decision algorithm which has better performance and/or lower complexity than the most effective existing algorithms (i.e. the Chase (1972) and Wolf algorithms). This makes the algorithm a strong candidate for applications where the advantage of high performance block codes are of interest, such as in packet radio and mobile radio systems. The new combined algorithm is ideally suited for DSP implementation.< >

关键词： Block codes Cyclic codes Decoding Signal processing

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