4,212 research outputs found
A neuro-fuzzy architecture for real-time applications
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
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
A neural network architecture for implementation of expert systems for real time monitoring
Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in
computations can be used to improve energy efficiency. We study the performance
of faulty implementations of certain deep neural networks based on pessimistic
and optimistic models of the effect of hardware faults. After identifying the
impact of hyperparameters such as the number of layers on robustness, we study
the ability of the network to compensate for computational failures through an
increase of the network size. We show that some networks can achieve equivalent
performance under faulty implementations, and quantify the required increase in
computational complexity
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Large-scale deep neural networks are both memory intensive and
computation-intensive, thereby posing stringent requirements on the computing
platforms. Hardware accelerations of deep neural networks have been extensively
investigated in both industry and academia. Specific forms of binary neural
networks (BNNs) and stochastic computing based neural networks (SCNNs) are
particularly appealing to hardware implementations since they can be
implemented almost entirely with binary operations. Despite the obvious
advantages in hardware implementation, these approximate computing techniques
are questioned by researchers in terms of accuracy and universal applicability.
Also it is important to understand the relative pros and cons of SCNNs and BNNs
in theory and in actual hardware implementations. In order to address these
concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the
universal approximation property with probability 1 (due to the stochastic
behavior). The proof is conducted by first proving the property for SCNNs from
the strong law of large numbers, and then using SCNNs as a "bridge" to prove
for BNNs. Based on the universal approximation property, we further prove that
SCNNs and BNNs exhibit the same energy complexity. In other words, they have
the same asymptotic energy consumption with the growing of network size. We
also provide a detailed analysis of the pros and cons of SCNNs and BNNs for
hardware implementations and conclude that SCNNs are more suitable for
hardware.Comment: 9 pages, 3 figure
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