6,772 research outputs found
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or
synapses, between neurons. This poses a problem as the primary computational
bottleneck for neural networks is the vector-matrix multiply when inputs are
multiplied by the neural network weights. Conventional processing architectures
are not well suited for simulating neural networks, often requiring large
amounts of energy and time. Additionally, synapses in biological neural
networks are not binary connections, but exhibit a nonlinear response function
as neurotransmitters are emitted and diffuse between neurons. Inspired by
neuroscience principles, we present a digital neuromorphic architecture, the
Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex
synaptic response functions without requiring additional hardware components.
We consider the paradigm of spiking neurons with temporally coded information
as opposed to non-spiking rate coded neurons used in most neural networks. In
this paradigm we examine liquid state machines applied to speech recognition
and show how a liquid state machine with temporal dynamics maps onto the
STPU-demonstrating the flexibility and efficiency of the STPU for instantiating
neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN
ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS IN BUSINESS
In modern software implementations of artificial neural networks the approach inspired by biology has more or less been abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks, or parts of neural networks (such as artificial neurons), are used as components in larger systems that combine both adaptive and non-adaptive elements. There are many problems which are solved with neural networks, especially in business and economic domains.neuron, neural networks, artificial intelligence, feed-forward neural networks, classification
Neuron as a reward-modulated combinatorial switch and a model of learning behavior
This paper proposes a neuronal circuitry layout and synaptic plasticity
principles that allow the (pyramidal) neuron to act as a "combinatorial
switch". Namely, the neuron learns to be more prone to generate spikes given
those combinations of firing input neurons for which a previous spiking of the
neuron had been followed by a positive global reward signal. The reward signal
may be mediated by certain modulatory hormones or neurotransmitters, e.g., the
dopamine. More generally, a trial-and-error learning paradigm is suggested in
which a global reward signal triggers long-term enhancement or weakening of a
neuron's spiking response to the preceding neuronal input firing pattern. Thus,
rewards provide a feedback pathway that informs neurons whether their spiking
was beneficial or detrimental for a particular input combination. The neuron's
ability to discern specific combinations of firing input neurons is achieved
through a random or predetermined spatial distribution of input synapses on
dendrites that creates synaptic clusters that represent various permutations of
input neurons. The corresponding dendritic segments, or the enclosed individual
spines, are capable of being particularly excited, due to local sigmoidal
thresholding involving voltage-gated channel conductances, if the segment's
excitatory and absence of inhibitory inputs are temporally coincident. Such
nonlinear excitation corresponds to a particular firing combination of input
neurons, and it is posited that the excitation strength encodes the
combinatorial memory and is regulated by long-term plasticity mechanisms. It is
also suggested that the spine calcium influx that may result from the
spatiotemporal synaptic input coincidence may cause the spine head actin
filaments to undergo mechanical (muscle-like) contraction, with the ensuing
cytoskeletal deformation transmitted to the axon initial segment where it
may...Comment: Version 5: added computer code in the ancillary files sectio
Comparison between Oja's and BCM neural networks models in finding useful projections in high-dimensional spaces
This thesis presents the concept of a neural network starting from its corresponding biological model, paying particular attention to the learning algorithms proposed by Oja and Bienenstock Cooper & Munro. A brief introduction to Data Analysis is then performed, with particular reference to the Principal Components Analysis and Singular Value Decomposition.
The two previously introduced algorithms are then dealt with more thoroughly, going to study in particular their connections with data analysis. Finally, it is proposed to use the Singular Value Decomposition as a method for obtaining stationary points in the BCM algorithm, in the case of linearly dependent inputs
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
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