Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems

Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Determinación de la denominación de origen de vinos chilenos basado en máquinas de soporte vectorial

Se presenta un método para determinar la denominación de origen de vinos chilenos basado en su concentración de metales. Se emplea un repositorio de 77 muestras de vinos y sus correspondientes concentraciones de metales. Se aplican dos funciones Kernel junto a clasificadores basados en Máquinas de Soporte Vectorial. Se comparan tres metaheurísticas para encontrar los hiperparámetros óptimos de los clasificadores. Para entrenarlos se aplica Validación Cruzada Dejando Uno Fuera. Los resultados se calculan en base al error promedio de las clasificaciones. Los porcentajes de error se estiman no superiores al 15 %, destacando la combinación de Recocido Simulado y Kernel Lineal como la más óptima.Sociedad Argentina de Informática e Investigación Operativ

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Expediting the accuracy-improving process of SVMs for class imbalance learning

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

HEALTH ESTIMATION AND REMAINING USEFUL LIFE PREDICTION OF ELECTRONIC CIRCUIT WITH A PARAMETRIC FAULT

Degradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values. Such parametric drifts in turn will cause degradation in performance of the circuit they are part of, eventually leading to function failure due to parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications where the components are integrated into a complex electronic circuit assembly, product or system, it is generally not feasible to monitor component-level parameters. To address this problem, a prognostics method that exploits features extracted from responses of circuit-comprising components exhibiting parametric faults is developed in this dissertation. The developed prognostic method constitutes a circuit health estimation step followed by a degradation modeling and remaining useful life (RUL) prediction step. First, the circuit health estimation method was developed using a kernel-based machine learning technique that exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The performance of kernel learning technique depends on the automatic adaptation of hyperparameters (i.e., regularization and kernel parameters) to the learning features. Thus, to achieve high accuracy in health estimation the developed method also includes an optimization method that employs a penalized likelihood function along with a stochastic filtering technique for automatic adaptation of hyperparameters. Second, the prediction of circuit’s RUL is realized by a model-based filtering method that relies on a first principles-based model and a stochastic filtering technique. The first principles-based model describes the degradation in circuit health with progression of parametric fault in a circuit component. The stochastic filtering technique on the other hand is used to first solve a joint ‘circuit health state—parametric fault’ estimation problem, followed by prediction problem in which the estimated ‘circuit health state—parametric fault’ is propagated forward in time to predict RUL. Evaluations of the data from simulation experiments on a benchmark Sallen–Key filter circuit and a DC–DC converter system demonstrate the ability of the developed prognostic method to estimate circuit health and predict RUL without having to monitor the individual component parameters