Genetic algorithm for identifying self-generating radial basis neural networks

Enab, Yehia M.

doi:10.1049/cp:19950530

Cited by 2 publications

(2 citation statements)

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“…The centers are optimized in [17]- [20], [23], [28]- [33], [37]- [42], [45], [46], [48]- [55], [57], [58], [62], [63], [65]- [71], and [73]- [79], for instance. Radii are considered in [17], [19]- [23], [26]- [28], [30], [32], [38], [45], [46], [50], [51], [54], [56], [58], [60], [62], [63], [65]- [69], [71], and [74]- [79], and weights in the output layer in [18], [19], [51], [56], [57], [60], and [61]. Particular parameters of basis function types (e.g., shaping parameters) are optimized in…”

Section: A Related Workmentioning

confidence: 99%

“…Most of the 64 publications (about 70%) utilize very small and often simple benchmark data sets (e.g., two spirals, trigonometric functions, polynomials, iris, glass, or sunspot). Real-world data is considered in 19 publications, e.g., from robotic applications [19], [24], [44], [64], medical or biometrical applications [39], [49], power systems modeling and control [46], [47], [61], or chemical applications [60]. Typically, these publications focus on one particular application problem.…”

Section: B Motivation For a New Approachmentioning

confidence: 99%

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Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Buchtala

Klimek²,

Sick

2005

IEEE Trans. Syst., Man, Cybern. B

108

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In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

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Section: A Related Workmentioning

confidence: 99%

Section: B Motivation For a New Approachmentioning

confidence: 99%

Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Buchtala

Klimek²,

Sick

2005

IEEE Trans. Syst., Man, Cybern. B

108

View full text Add to dashboard Cite

show abstract

Non-linear system identification using an artificial immune system

Luh

Cheng

2001

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this paper, a novel non-linear system identi cation methodology is developed employing the features of the arti cial immune system. A simpli ed incremental approach integrated with the maximum entropy principle and an instantaneous feedback mechanism is proposed to reorganize the system's parameters simultaneously. To verify and demonstrate the eVectiveness of the proposed algorithm, a simulation example on a two-link robot was studied. This algorithm can achieve robustness and eYciency in identifying complex non-linear systems. The simulation results show that the identi ed immune models are robust to noise and various uncertainties in the robot dynamics.

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Genetic algorithm for identifying self-generating radial basis neural networks

Cited by 2 publications

References 0 publications

Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Non-linear system identification using an artificial immune system

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