System identification and linearisation using genetic algorithms with simulated annealing

Tan, Kay Chen; Li, Y.; Murray-Smith, D.J.; Sharman, Ken

doi:10.1049/cp:19951043

Cited by 34 publications

(22 citation statements)

References 4 publications

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“…Note that the population size of 40 is rather small compared to the population size of 100 adopted in Jean and Chen [21] and the population size of 150 adopted in Tan et al [12]. From the previous sections, the stuck number N frozen is chosen to 10 or 20 for the proposed method, ARSAGA.…”

Section: System Identificationmentioning

confidence: 99%

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Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Hwang

2006

Advances in Engineering Software

117

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Section: System Identificationmentioning

confidence: 99%

“…This combined algorithm can keep the advantages and avoid the disadvantages of both search algorithms. Owing to the existence of SA, this combined algorithm has better fine-tuning ability to search for the global optimum solutions and more strong hill-climbing ability for escaping from local minima than the standard GA [7,12].…”

Section: Introductionmentioning

confidence: 99%

Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Hwang

2006

Advances in Engineering Software

117

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“…Tan et al (1995) used a GA to develop polynomial ARMAX (Auto Regressive Moving Average with eXogenous inputs) model structures. The technique was rather limited, as the model structure was predetermined, with the GA being used to optimise the model parameters and the maximum time shift of the input.…”

Section: Advantages Of Using Genetic Algorithmsmentioning

confidence: 99%

Dynamic systems modelling using genetic programming

Hinchliffe¹,

Willis²

2003

Computers & Chemical Engineering

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Genetic programming (GP) is an evolutionary algorithm that attempts to evolve solutions to a problem by using concepts taken from the naturally occurring evolutionary process. This thesis introduces the concepts of GP model development by applying the technique to steady-state model evolution. A variation of the algorithm known as the multiple basis function GP (MBF-GP) algorithm is described and its performance compared with the standard algorithm. Results show that the MBF-GP algorithm requires significantly less computational effort to evolve models of comparable accuracy to the standard algorithm. The steady-state algorithm is then modified to enable the evolution of dynamic process models. Three case studies are used to demonstrate algorithm performance and show how the MBF-GP algorithm produces performance benefits similar to those observed in the steady-state modelling work. A comparison with neural networks reveals that GP is able to match the accuracy of the network predictions but is more expensive computationally. However, a significant advantage of the GP algorithm is that it can automatically evolve the time history of model terms required to account for process characteristics such as the system time delay.The model development process is not simply a case of reducing the error between the predicted and actual process output. The parallel nature of GP means that it is ideally suited to solving multi-objective problems. The MBF-GP algorithm is modified to incorporate a Pareto based ranking scheme that allows models to be compared using multiple performance criteria. The ranking scheme allows preference information in the form of goals and priorities to be specified in order to guide the search towards the desired region of the search space. Two case studies are used to demonstrate the performance of this technique. The first example uses the multi-objective algorithm to improve the parsimony of the evolved model structures. The second example demonstrates how a set residual correlation tests can be combined and used as an additional performance measure. In each case, the multi-objective algorithm performs significantly better than the single objective version. In addition, the inclusion of preference information overcomes some of the difficulties associated with conventional Pareto ranking and produces a greater number of acceptable solutions. Prefaceii

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“…However, it is well-known that existing GAs are weak in local exploration and are thus poor in finding the exact optima at each generation (Tan et al, 1995). The underlying reason of this is that, in a pure GA, there is a lack of "biological diversity" resulting from interactions with, and thus direct learning from, the evolution environment, termed the "Lamarckism inheritance" (Aboitiz, 1992).…”

Section: Boltzmann Learning Enhanced Genetic Algorithmmentioning

confidence: 99%

Evolutionary L∞ identification and model reduction for robust control

Tan

2000

Proceedings of the Institution of Mechanical Engineers, Part I:

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System identification and linearisation using genetic algorithms with simulated annealing

Cited by 34 publications

References 4 publications

Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Dynamic systems modelling using genetic programming

Evolutionary L∞ identification and model reduction for robust control

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