The proceedings contains 5 papers on AI applications in power systems. Topics discussed include expert systems, decision support systems, electric power system protection, voltage control, neural networks, genetic alg...
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The proceedings contains 5 papers on AI applications in power systems. Topics discussed include expert systems, decision support systems, electric power system protection, voltage control, neural networks, geneticalgorithms, planning, systems analysis and mathematical models.
The proceedings contains 91 papers from the first iee/ieeE International Conference on geneticalgorithms in engineeringsystems. Topics discussed include: aerospace and automotive applications;optimization;scheduling...
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The proceedings contains 91 papers from the first iee/ieeE International Conference on geneticalgorithms in engineeringsystems. Topics discussed include: aerospace and automotive applications;optimization;scheduling;system identification;applications in electric power systems;controlsystems design;neural networks;digital filter design;intelligent machines and robotics;signal processing;mathematical techniques and models;manufacturing;genetic programming;parallel geneticalgorithms;and fault detection.
This paper deals with the application of geneticalgorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the ...
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This paper deals with the application of geneticalgorithms for optimizing the parameters of conventional automatic generation control (AGC) systems. A two-area nonreheat thermal system is considered to exemplify the optimum parameter search. A digital simulation is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the integral of time-multiplied absolute value of the error performance indices are considered in the search for the optimal AGC parameters. The results reported in this paper demonstrate the effectiveness of the geneticalgorithms in the tuning of the AGC parameters.
genetic Algorithm (GA) is a stochastic adaptive algorithm whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. The GA has been adapted to study the problem of ...
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genetic Algorithm (GA) is a stochastic adaptive algorithm whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. The GA has been adapted to study the problem of designing a stable sliding mode which yields robust performance in variable structure controlsystems. For various cases, we show that GA is viable and has great potential in the design of sliding mode controlsystems. Assigning eigenvalues in a specified fringe yields better results than assignment at exact locations.
This paper presents an overview of the theoretical foundations of the genetic algorithm. It aims to provide those interested in applying GAs to real-world problems with a feel for the techniques that can be employed t...
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This paper presents an overview of the theoretical foundations of the genetic algorithm. It aims to provide those interested in applying GAs to real-world problems with a feel for the techniques that can be employed to analyze and controlgenetic-based optimizers and controlsystems. In addition to a review of schema, Walsh-function and statistical mechanics-based methods the paper introduces a model of selection based on statistics of the population fitness distribution.
Some problems are very difficult to solve by mathematical programming approaches. A genetic algorithm (GA) is an extremely powerful optimization technique that could be used to solve such problems, but its efficiency ...
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Some problems are very difficult to solve by mathematical programming approaches. A genetic algorithm (GA) is an extremely powerful optimization technique that could be used to solve such problems, but its efficiency is dependent on its ability to do a large number of evaluations in a reasonable amount of time. A classical GA contains three basic operators - reproduction, crossover, and mutation. To increase the efficiency of a genetic algorithm the influence of migration in a multilevel distributed GA (MDGA) was tested. Several different structures of PC- computers connected in a local area network (LAN) were used for the MDGAs. MDGAs the use power of the computers better than one level distributed GAs. The problem of communication between the computers in the MDGAs was dealt with in two different ways: with files on a server or by sending packets.
The selection of an appropriate set of manipulated variables to control a set of specified outputs is an important aspect of MIMO system design. This paper details how geneticalgorithms may be used to provide an auto...
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The selection of an appropriate set of manipulated variables to control a set of specified outputs is an important aspect of MIMO system design. This paper details how geneticalgorithms may be used to provide an automated optimization procedure for the selection of the input-output pairings based upon the Relative Gain Array (RGA). Two examples are outlined to demonstrate the effectiveness of the proposed strategy.
In this contribution a tree structured genetic algorithm is described. The algorithm is used to generate non-linear models from process input-output data. Three examples are utilized to demonstrate the applicability o...
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In this contribution a tree structured genetic algorithm is described. The algorithm is used to generate non-linear models from process input-output data. Three examples are utilized to demonstrate the applicability of the technique within the domain of process engineering.
The Classifier System (CS) is a machine learning process: the machine (a computer program) learns about a particular environment and is then capable of making beneficial decisions or predictions concerning that enviro...
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The Classifier System (CS) is a machine learning process: the machine (a computer program) learns about a particular environment and is then capable of making beneficial decisions or predictions concerning that environment. The learning method within a CS involves the use of a genetic Algorithm (GA). An attempt to solve a civil engineering problem, using a genetic based learning algorithm is presented. More specifically, it explains the research being undertaken at the University of Wales regarding the Derivation of Reservoir control Strategies using a form of CS.
Loudspeaker systems employing more than one driver exhibit off-axis cancellation, which colours the sound heard by the listener. By using knowledge of how the human hearing system works it is possible to hide the erro...
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Loudspeaker systems employing more than one driver exhibit off-axis cancellation, which colours the sound heard by the listener. By using knowledge of how the human hearing system works it is possible to hide the errors from the listener, thus giving perfect sound. The design of the filters necessary to achieve the error concealment is a difficult task, and in this paper a method is proposed which employs geneticalgorithms.
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