Multilayer feedforward networks are universal approximators
Neural Networks
Robotics and Autonomous Systems
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Principal Manifolds for Data Visualization and Dimension Reduction
Principal Manifolds for Data Visualization and Dimension Reduction
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
Classification of audio signals using AANN and GMM
Applied Soft Computing
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
Robotics and Autonomous Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Acoustic sensing to gather information about a machine can be highly beneficial, but processing the data can be difficult. In this work, a variety of methodologies have been studied to extract rotor speed information from the sound signature of an autonomous helicopter, with no a-priori knowledge of its underlying acoustic properties. The autonomous helicopter has two main rotors that are mostly identical. In order to identify the rotors' speeds individually, a comparative evaluation has been made of learning methods with input selection, reduction and aggregation methods. The resulting estimators have been tested on unseen training data as well as in actual free-flight tests. The best results were found by using a genetic algorithm to identify the important frequency bands, a centroid method to aggregate the bands, and a neural-network estimator of the rotor speeds. This approach succeeded in estimating individual rotor speeds of an autonomous helicopter without being distracted by the other, mainly identical, rotor. These results were achieved using standard, low-cost hardware, and a learning approach that did not require any a-priori knowledge about the system's acoustic properties.