83,051 research outputs found
A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods
The purpose of this paper is to present two different
approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a
more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services
A Possibilistic and Probabilistic Approach to Precautionary Saving
This paper proposes two mixed models to study a consumer's optimal saving in
the presence of two types of risk.Comment: Panoeconomicus, 201
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Variable grouping in multivariate time series via correlation
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
Quantum canonical tensor model and an exact wave function
Tensor models in various forms are being studied as models of quantum
gravity. Among them the canonical tensor model has a canonical pair of
rank-three tensors as dynamical variables, and is a pure constraint system with
first-class constraints. The Poisson algebra of the first-class constraints has
structure functions, and provides an algebraically consistent way of
discretizing the Dirac first-class constraint algebra for general relativity.
This paper successfully formulates the Wheeler-DeWitt scheme of quantization of
the canonical tensor model; the ordering of operators in the constraints is
determined without ambiguity by imposing Hermiticity and covariance on the
constraints, and the commutation algebra of constraints takes essentially the
same from as the classical Poisson algebra, i.e. is first-class. Thus one could
consistently obtain, at least locally in the configuration space, wave
functions of "universe" by solving the partial differential equations
representing the constraints, i.e. the Wheeler-DeWitt equations for the quantum
canonical tensor model. The unique wave function for the simplest non-trivial
case is exactly and globally obtained. Although this case is far from being
realistic, the wave function has a few physically interesting features; it
shows that locality is favored, and that there exists a locus of configurations
with features of beginning of universe.Comment: 17 pages. Section 2 expanded to include fuzzy-space interpretation,
and other minor change
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