Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented.

No description available.

This thesis considers the neural network learning control of a variable-geometry automotive active suspension system which combines most of the benefits of active suspension systems with low energy consumption. Firstly, neural networks are applied to the control of various simplified automotive active suspensions, in order to understand how a neural network controller can be integrated with a physical dynamic system model. In each case considered, the controlled system has a defined objective and the minimisation of a cost function. The neural network is set up in a learning structure, such that it systematically improves the system performance via repeated trials and modifications of parameters. The learning efficiency is demonstrated by the given system performance in agreement with prior results for both linear and non-linear systems. The above simulation results are generated by MATLAB and the Neural Network Toolbox. Secondly, a half-car model, having one axle and an actuator on each side, is developed via the computer language, AUTOSIM. Each actuator varies the ratio of the spring/damper unit length change to wheel displacement in order to control each wheel rate. The neural network controller is joined with the half-car model and learns to reduce the defined cost function containing a weighted sum of the squares of the body height change, body roll and actuator displacements. The performances of the neurocontrolled system are compared with those of passive and proportional-plusdifferential controlled systems under various conditions. These involve various levels of lateral force inputs and vehicle body weight changes. Finally, energy consumption of the variable-geometry system, with either the neurocontrol or proportional-plus-differential control, is analysed using an actuator model via the computer simulation package, SIMULINK. The simulation results are compared with those of other actively-controlled suspension systems taken from the literature.

<p>In this work new artificial learning and innate control mechanisms are proposed for application</p><p>in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)</p><p>existent in the literature is enhanced with respect to its capacity of exploring the environment and</p><p>avoiding risky configurations (that lead to collisions with obstacles even after learning). The</p><p>particular autonomous system is based on modular hierarchical neural networks. Initially,the</p><p>autonomous system does not have any knowledge suitable for exploring the environment (and</p><p>capture targets œ foraging). After a period of learning,the system generates efficientobstacle</p><p>avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous</p><p>system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky</p><p>configurations) are discussed and the new learning and controltechniques (applied to the</p><p>autonomous system) are verified through simulations. It is shown the effectiveness of the</p><p>proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and</p><p>decrease their probability of appearance in the future and the number of collisions in risky</p><p>situations is significantly decreased. Experiments also consider maze environments (with targets</p><p>distant from each other) and dynamic environments (with moving objects).</p>

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence, the segmentation of the data into classes that consist on the sets of points best approximated by each model, and second, the computation of the normalized discriminant functions for each induced class. The approximating function may then be computed as the optimal estimator with respect to this measure field. We give an efficient procedure for effecting both computations, and for the determination of the optimal number of components.

The purpose of this thesis was to delineate the neural underpinning of three types of declarative memory retrieval; autobiographical, episodic, and semantic. Autobiographical memory was defined as the conscious recollection of personally relevant events, episodic memory as the recall of stimuli presented in the laboratory, and semantic memory as the retrieval of factual information and general knowledge about the world. Young adults participated in an event-related fMRI study in which pictorial stimuli were presented as cues for retrieval. By manipulating retrieval demands, autobiographical, episodic, or semantic memories were extracted in response to the same stimulus. The objective of the subsequent analyses was threefold: firstly, to delineate regional activations common across the memory conditions, as well as neural activations unique to each memory type (“condition-specific”); secondly, to delineate a functional network common to all three memory conditions; and, thirdly, to delineate functional network(s) of brain regions that show condition-specific activity and to assess their overlap with the common functional network. The results of the first analysis showed regional activations common to all three types of memory retrieval in the bilateral inferior frontal gyrus, left middle frontal gyrus, right caudate nucleus, bilateral thalamus, left hippocampus, and left lingual gyrus. Condition-specific activations were also delineated, including medial frontal increases for autobiographical, right middle frontal increases for episodic, and right inferior temporal increases for semantic retrieval. The second set of analyses delineated a functional network common to the three conditions that comprised 21 functionally connected neural areas. The final set of analyses further explored the functional connectivity of those brain regions that showed condition-specific activations, yielding two functional networks – one involved semantic and autobiographical conditions, and the other involved episodic and autobiographical conditions. Despite their recruiting some brain regions unique to the content of retrieved memories, the two functional networks did overlap to a degree with the common functional network. Together, these findings lend support to the notion of a common network, which is hypothesized to give rise to different types of declarative memory retrieval (i.e., autobiographical, episodic, or semantic) along a contextual continuum (i.e., highly contextualized or highly decontextualized).

The purpose of this thesis was to delineate the neural underpinning of three types of declarative memory retrieval; autobiographical, episodic, and semantic. Autobiographical memory was defined as the conscious recollection of personally relevant events, episodic memory as the recall of stimuli presented in the laboratory, and semantic memory as the retrieval of factual information and general knowledge about the world. Young adults participated in an event-related fMRI study in which pictorial stimuli were presented as cues for retrieval. By manipulating retrieval demands, autobiographical, episodic, or semantic memories were extracted in response to the same stimulus. The objective of the subsequent analyses was threefold: firstly, to delineate regional activations common across the memory conditions, as well as neural activations unique to each memory type (“condition-specific”); secondly, to delineate a functional network common to all three memory conditions; and, thirdly, to delineate functional network(s) of brain regions that show condition-specific activity and to assess their overlap with the common functional network. The results of the first analysis showed regional activations common to all three types of memory retrieval in the bilateral inferior frontal gyrus, left middle frontal gyrus, right caudate nucleus, bilateral thalamus, left hippocampus, and left lingual gyrus. Condition-specific activations were also delineated, including medial frontal increases for autobiographical, right middle frontal increases for episodic, and right inferior temporal increases for semantic retrieval. The second set of analyses delineated a functional network common to the three conditions that comprised 21 functionally connected neural areas. The final set of analyses further explored the functional connectivity of those brain regions that showed condition-specific activations, yielding two functional networks – one involved semantic and autobiographical conditions, and the other involved episodic and autobiographical conditions. Despite their recruiting some brain regions unique to the content of retrieved memories, the two functional networks did overlap to a degree with the common functional network. Together, these findings lend support to the notion of a common network, which is hypothesized to give rise to different types of declarative memory retrieval (i.e., autobiographical, episodic, or semantic) along a contextual continuum (i.e., highly contextualized or highly decontextualized).

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 76-80).

This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-04-23 11:59:07.806