In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...

This paper proposes a simple test for global identi…cation of Generalized Method of Moments (GMM) estimators in models de…ned by conditional moment restrictions. The test is based on comparing two estimators, one which is always consistent, even under global identi…cation failure of the unconditional model, and the GMM estimator. It is shown that the test is able to detect Pitman alternatives, ...

We describe our system in 2014 LifeCLEF [1] Plant Identification Competition. The sub-system for isolated leaf category (LeafScans) was basically the same as last year [2], while plant photographs in all the remaining categories were classified using either local descriptors or deep learning techniques. However, due to large amount of data, large number of classes and shortage of time, our syst...

In the present paper strong{monotonic, monotonic and weak{monotonic reasoning is studied in the context of algorithmic language learning theory from positive as well as from positive and negative data. Strong{monotonicity describes the requirement to only produce better and better generalizations when more and more data are fed to the inference device. Monotonic learning re ects the eventual in...

We have already developed a speech analysis method based on the Glottal-ARMAX (Auto Regressive and Moving Average eXogenous) model, in which the speech production model is supposed to be an ARMAX vocal tract model and two kinds of excitation: glottal source model excitation and white Gaussian. The speech analysis method based on the Glottal-ARMAX model can estimate the glottal source and ARMAX ...

The number of languages in the world is much larger than the number of target languages that current language identication systems can handle. Therefore, we propose here the use of a multilayer perceptron neural network as a means to prevent those unknown language inputs from being misidenti ed as one of the target languages. We consider not only the target language identi cation rate but also ...

This paper studies simultaneous equations models for two or more discrete outcomes. The models allow latent variables to be high dimensional and require latent variables and covariates to be independently distributed. The models may be incoherent, delivering no values of the outcomes at certain values of the latent variables and covariates. The models may be incomplete, delivering more than one...