Adaptive Tesselation CMAC

An ndaptive tessellation variant of the CMAC architecture is introduced. Adaptive tessellation is an error-based scheme for distributing input representations. Simulations show that the new network outperforms the original CMAC at a vnriety of learning tasks, including learning the inverse kinematics of a two-link arm.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100); National Science Foundation (IRI-90-00530); Boston University Presidential Graduate Fellowshi

Financial distress prediction using the hybrid associative memory with translation

This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been explored in any depth as yet. The performance of the hybrid associative memory with translation is compared to four traditional neural networks, a support vector machine and a logistic regression model in terms of their prediction capabilities. The experimental results over nine real-life data sets show that the associative memory here proposed constitutes an appropriate solution for bankruptcy and credit risk prediction, performing significantly better than the rest of models under class imbalance and data overlapping conditions in terms of the true positive rate and the geometric mean of true positive and true negative rates.This work has partially been supported by the Mexican CONACYT through the Postdoctoral Fellowship Program [232167], the Spanish Ministry of Economy [TIN2013-46522-P], the Generalitat Valenciana [PROMETEOII/2014/062] and the Mexican PRODEP [DSA/103.5/15/7004]. We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Interactive-time vision--face recognition as a visual behavior

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 1991.Includes bibliographical references (leaves 107-115).by Matthew Alan Turk.Ph.D

Joint time-frequency analysis and filtering of single trial event-related potentials.

The ongoing electrical activity of the brain is known as the electroencephalograph (EEG). Event related potentials (ERPs) are voltage deviations in the EEG elicited in association with stimuli. Their elicitation require cognitive processes such as response to a recognised stimulus. ERPs therefore provide clinical information by allowing an insight into neurological processes. The amplitude of an event-related potential is typically several times less than the background EEG. The background EEG has the effect of obscuring the ERP and therefore appropriate signal processing is required for its recovery. Traditionally ERPs are estimated using the synchronised averaging of several single trials or sweeps. This inhibits investigation of any trial-to-trial variation, which can prove valuable in understanding cognitive processes. An aim of this study was to develop wavelet-based techniques for the recovery of single trial ERPs from background EEG. A novel wavelet-based adaptive digital filtering method for ERPs has been developed. The method provides the ability to effectively estimate or recover single ERPs. The effectiveness of the method has been quantitatively evaluated and compared with other methods of ERP estimation.The ability to recover single sweep ERPs allowed the investigation of characteristics that are not possible using the conventional averaged estimation. The development of features of a cognitive ERP known as the contingent negative variation over a number of trials was investigated. The trend in variation enabled the identification of schizophrenic subjects using artificial intelligence methods.A new technique to investigate the phase dynamics of ERPs was developed. This was successfully applied, along with other techniques, to the investigation of independent component analysis (ICA) component activations in a visual spatial attention task. Two components with scalp projections that suggested that they may be sources within the visual cortex were investigated. The study showed that the two components were visual field selective and that their activation was both amplitude and phase modulated

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

A multiresolution learning method for back-propagation networks.

Wing-Chung Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 81-85).Chapter 1 --- Introduction --- p.1Chapter 2 --- Multiresolution Signal Decomposition --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- Laplacian Pyramid --- p.6Chapter 2.2.1 --- Gaussian Pyramid Generation --- p.7Chapter 2.2.2 --- Laplacian Pyramid Generation --- p.7Chapter 2.2.3 --- Decoding --- p.8Chapter 2.2.4 --- Limitation --- p.9Chapter 2.3 --- Multiresolution Transform --- p.9Chapter 2.3.1 --- Multiresolution Approximation of L2(R) --- p.9Chapter 2.3.2 --- Implementation of a Multiresolution Transform --- p.12Chapter 2.3.3 --- Orthogonal Wavelet Representation --- p.16Chapter 2.3.4 --- Implementation of an Orthogonal Wavelet Representation --- p.18Chapter 2.3.5 --- Signal Reconstruction --- p.21Chapter 3 --- Multiresolution Learning Method --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Input Vector Representation --- p.24Chapter 3.2.1 --- Representation at the resolution 1 --- p.24Chapter 3.2.2 --- Representation at the resolution 2j --- p.25Chapter 3.2.3 --- Border Problem --- p.26Chapter 3.3 --- Back-Propagation Network Architecture --- p.26Chapter 3.4 --- Training Procedure Strategy --- p.27Chapter 3.4.1 --- Sum Squared Error (SSE) --- p.28Chapter 3.4.2 --- Intermediate Stopping Criteria --- p.30Chapter 3.5 --- Connection Weight Transformation --- p.31Chapter 3.5.1 --- Weights between the Input and Hidden Layers --- p.31Chapter 3.5.2 --- Weights between the Hidden and Output Layers --- p.33Chapter 4 --- Simulations --- p.36Chapter 4.1 --- Introduction --- p.36Chapter 4.2 --- Choices of the Impulse Response h(n) --- p.36Chapter 4.3 --- XOR Problem --- p.39Chapter 4.3.1 --- Setting of Experiments --- p.39Chapter 4.3.2 --- Experimental Results --- p.41Chapter 4.4 --- Numeric Recognition Problem --- p.50Chapter 4.4.1 --- Setting of Experiments --- p.50Chapter 4.4.2 --- Experimental Results --- p.52Chapter 4.5 --- Discussions --- p.72Chapter 5 --- Conclusions --- p.75Chapter A --- Proof of Equation (4.9) --- p.77Chapter B --- Proof of Equation (4.11) --- p.79Bibliography --- p.8