12 research outputs found

    Mixed-Signal VLSI Implementation of CVNS Artificial Neural Networks

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    In this work, mixed-signal implementation of Continuous Valued Number System (CVNS) neural network is proposed. The proposed network resolves the limited signal processing precision issue present in mixed-signal neural networks. This is realized by the CVNS addition, the CVNS multiplication and the CVNS sigmoid function evaluation algorithms proposed in this dissertation. The proposed algorithms provide accurate results in low-resolution environment. In addition, an area-efficient low sensitivity CVNS Madaline is proposed. The proposed Madaline is more robust to input and weight errors when compared to the previously developed structures. Moreover, its area consumption is lower. Furthermore, a new approximation scheme for hyperbolic tangent activation function is proposed. Using the proposed approximation scheme results in efficient implementation of digital ASIC neural networks in terms of area, delay and power consumption

    Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks

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    In this paper, the output reachable estimation and safety verification problems for multi-layer perceptron neural networks are addressed. First, a conception called maximum sensitivity in introduced and, for a class of multi-layer perceptrons whose activation functions are monotonic functions, the maximum sensitivity can be computed via solving convex optimization problems. Then, using a simulation-based method, the output reachable set estimation problem for neural networks is formulated into a chain of optimization problems. Finally, an automated safety verification is developed based on the output reachable set estimation result. An application to the safety verification for a robotic arm model with two joints is presented to show the effectiveness of proposed approaches.Comment: 8 pages, 9 figures, to appear in TNNL

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

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    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

    Quarterly research review no. 11, 1 october - 31 december 1964

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    Plasma, quantum electronics, system theory and electron tube studie

    Applications of artificial intelligence techniques to a spacecraft control problem

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    Artificial intelligence applied to spacecraft control proble
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