29,329 research outputs found
On the possible Computational Power of the Human Mind
The aim of this paper is to address the question: Can an artificial neural
network (ANN) model be used as a possible characterization of the power of the
human mind? We will discuss what might be the relationship between such a model
and its natural counterpart. A possible characterization of the different power
capabilities of the mind is suggested in terms of the information contained (in
its computational complexity) or achievable by it. Such characterization takes
advantage of recent results based on natural neural networks (NNN) and the
computational power of arbitrary artificial neural networks (ANN). The possible
acceptance of neural networks as the model of the human mind's operation makes
the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of
Liverpool, UK. 23 page
Fast non-recursive extraction of individual harmonics using artificial neural networks
A collaborative work between Northumbria University and University of Peradeniya (Sri Lanka). It presents a novel technique based on Artificial Neural Networks for fast extraction of individual harmonic components. The technique was tested on a real-time hardware platform and results obtained showed that it is significantly faster and less computationally complex than other techniques. The paper complements other publications by the author (see paper 1) on the important area of âPower Qualityâ of electric power networks. It involves the application of advanced techniques in artificial intelligence to solve power systems problems
Discontinuities in recurrent neural networks
This paper studies the computational power of various discontinuous
real computational models that are based on the classical analog
recurrent neural network (ARNN). This ARNN consists of finite number
of neurons; each neuron computes a polynomial net-function and a
sigmoid-like continuous activation-function.
The authors introducePostprint (published version
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
How neural networks learn to classify chaotic time series
Neural networks are increasingly employed to model, analyze and control
non-linear dynamical systems ranging from physics to biology. Owing to their
universal approximation capabilities, they regularly outperform
state-of-the-art model-driven methods in terms of accuracy, computational
speed, and/or control capabilities. On the other hand, neural networks are very
often they are taken as black boxes whose explainability is challenged, among
others, by huge amounts of trainable parameters. In this paper, we tackle the
outstanding issue of analyzing the inner workings of neural networks trained to
classify regular-versus-chaotic time series. This setting, well-studied in
dynamical systems, enables thorough formal analyses. We focus specifically on a
family of networks dubbed Large Kernel Convolutional Neural Networks (LKCNN),
recently introduced by Boull\'{e} et al. (2021). These non-recursive networks
have been shown to outperform other established architectures (e.g. residual
networks, shallow neural networks and fully convolutional networks) at this
classification task. Furthermore, they outperform ``manual'' classification
approaches based on direct reconstruction of the Lyapunov exponent. We find
that LKCNNs use qualitative properties of the input sequence. In particular, we
show that the relation between input periodicity and activation periodicity is
key for the performance of LKCNN models. Low performing models show, in fact,
analogous periodic activations to random untrained models. This could give very
general criteria for identifying, a priori, trained models that have poor
accuracy
Adaptive inferential sensors based on evolving fuzzy models
A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can a- ddress the challenges of the modern advanced process industry
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