A Genetic Algorithm with Disruptive Selection - Systems, Man and Cybernetics, Part B, IEEE Transactions on
نویسندگان
چکیده
This paper combines a conventional method of multivariablesystem identification with a dynamic multi-layer perceptron (MLP) toachieve a constructive method of nonlinear system identification. Theclassof nonlinear systems is assumed to operate nominally around anequilibrium point in the neighborhood of which a linearized model existsto represent the system, although normal operation is not limited to thelinear region. The result is an accurate discrete-time nonlinear model,extended from aMIMO linear model, which captures the nonlinearbehavior of the system.
منابع مشابه
Genetic-based search for error-correcting graph isomorphism
Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selecti...
متن کاملSelection of relevant features in a fuzzy genetic learning algorithm
Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with la...
متن کاملWrapper-Filter Feature Selection Algorithm Using a Memetic Framework
This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate ...
متن کاملSwitching between selection and fusion in combining classifiers: an experiment
This paper presents a combination of classifier selection and fusion by using statistical inference to switch between the two. Selection is applied in those regions of the feature space where one classifier strongly dominates the others from the pool [called clustering-and-selection or (CS)] and fusion is applied in the remaining regions. Decision templates (DT) method is adopted for the classi...
متن کاملDynamic fuzzy control of genetic algorithm parameter coding
An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically in...
متن کامل