NARX-based nonlinear system identification using orthogonal least squares basis hunting

An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, whichplaces the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method isadopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance

A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. However, the Ant-miner is less class focused since the rule’s class is assigned after the rule was constructed. There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. Benchmark data sets from various fields were used to test the proposed algorithms. Experimental results obtained from this proposed algorithm are comparable to the results of the Ant-miner and other well-known rule induction algorithms in terms of rule accuracy, but are better in terms of rule simplicity. The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. The algorithm fixes the class before rule's construction. Since the algorithm fixed the class before each rule's construction, a much simpler heuristic and fitness function is proposed. Experimental results obtained from the proposed algorithm are much higher than other compared algorithms, in terms of predictive accuracy. The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. Thus, a higher predictive power classification model for various fields could be generated

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately

Adaptive simulated annealing for optimization in signal processing applications

Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations. The adaptive simulated annealing (ASA) offers a viable optimization tool for tackling these difficult nonlinear optimization problems. Three applications, maximum likelihood (ML) joint channel and data estimation, infinite-impulse-response (IIR) filter design and evaluation of minimum symbol-error-rate (MSER) decision feedback equalizer (DFE), are used to demonstrate the effectiveness of the ASA

Methods and algorithms for quantitative analysis of metallomic images to assess traumatic brain injury

The primary aim of this thesis is to develop image processing algorithms to quantitatively determine the link between traumatic brain injury (TBI) severity and chronic traumatic encephalopathy (CTE) neuropathology, specifically looking into the role of blood-brain barrier disruption following TBI. In order to causally investigate the relationship between the tau protein neurodegenerative disease CTE and TBI, mouse models of blast neurotrauma (BNT) and impact neurotrauma (INT) are investigated. First, a high-speed video tracking algorithm is developed based on K-means clustering, active contours and Kalman filtering to comparatively study the head kinematics in blast and impact experiments. Then, to compare BNT and INT neuropathology, methods for quantitative analysis of macroscopic optical images and fluorescent images are described. The secondary aim of this thesis focuses on developing methods for a novel application of metallomic imaging mass spectrometry (MIMS) to biological tissue. Unlike traditional modalities used to assess neuropathology, that suffer from limited sensitivity and analytical capacity, MIMS uses a mass spectrometer -- an analytical instrument for measuring elements and isotopes with high dynamic range, sensitivity and specificity -- as the imaging sensor to generate spatial maps with spectral (vector-valued) data per pixel. Given the vector nature of MIMS data, a unique end-to-end processing pipeline is designed to support data acquisition, visualization and interpretation. A novel multi-modal and multi-channel image registration (MMMCIR) method using multi-variate mutual information as a similarity metric is developed in order to establish correspondence between two images of arbitrary modality. The MMMCIR method is then used to automatically segment MIMS images of the mouse brain and systematically evaluate the levels of relevant elements and isotopes after experimental closed-head impact injury on the impact side (ipsilateral) and opposing side (contralateral) of the brain. This method quantifiably confirms observed differences in gadolinium levels for a cohort of images. Finally, MIMS images of human lacrimal sac biopsy samples are used for preliminary clinicopathological assessments, supporting the utility of the unique insights MIMS provides by correlating areas of inflammation to areas of elevated toxic metals. The image processing methods developed in this work demonstrate the significant capabilities of MIMS and its role in enhancing our understanding of the underlying pathological mechanisms of TBI and other medical conditions.2019-07-09T00:00:00