In this paper, the performance of the commonly used neural-network-based classifiers is investigated on solving a classification problem which aims to identify the object nature based on surface features of the object.When the surface data is obtained, a proposed feature extraction method is used to extract the surface feature of the object. The extracted features are then used as the inputs for the classifier. This research studies eighteen household objects which are requisite to our daily life. Six commonly used neural-network-based classifiers, namely one-against-all, weighted one-against-all, binary coded, parallel-structured, weighted parallel structured and tree-structured, are investigated. The performance for the six neural-network-based classifiers is evaluated based on recognition accuracy for individual object. Also, two traditional classifiers, namely k-nearest neighbor classifier and naive Bayes classifier, are employed for the comparison purposes. To evaluate robustness property of the classifiers, the original clean data is contaminated with Gaussian white noise. Experimental results show that the parallel-structured, tree-structured and the naive Bayes classifiers outperform the others under the noise-free data. The tree-structured classifier demonstrates the best robustness property under the noisy data.
This paper presents the automatic drug administration for the regulation of bispectral (BIS) index in the anesthesia process during the clinical surgery by controlling the concentration target of two drugs, namely, propofol and remifentanil. To realize the automatic drug administration, real clinical data are collected for 42 patients for the construction of patients' models consisting of pharmacokinetic and pharmacodynamic models describing the dynamics reacting to the input drugs. A nominal anesthesia model is obtained by taking the average of 42 patients' models for the design of control scheme. Three PID controllers are employed, namely linear PID controller, type-1 (T1) fuzzy PID controller and interval type-2 (IT2) fuzzy PID controller, to regulate the BIS index using the nominal patient's model. The PID gains and membership functions are obtained using genetic algorithm (GA) by minimizing a cost function measuring the control performance. The best trained PID controllers are tested under different scenarios and compared in terms of control performance. Simulation results show that the IT2 fuzzy PID controller offers the best control strategy regulating the BIS index while the T1 fuzzy PID controller comes the second.
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