Neuro-fuzzy models have a proven record of successful application in finance. Forecasting
future values is a crucial element of successful decision making in trading. In this paper, a …

This paper proposes an improved stochastic second order learning algorithm for supervised
neural network training. The proposed algorithm, named bounded stochastic diagonal …

This paper examines conditions under which the Resilient Propagation algorithm, Rprop,
fails to converge, identifies limitations of the so-called Globally Convergent Rprop algorithm …

Catalytic wet air oxidation (CWAO) process is employed for the treatment of N-tert-butyl-2-
benzothiazolesulfenamide (TBBS) wastewater in a microchannel reactor that enables …

Automatic speech recognition applications can benefit from a confidence measure (CM) to
predict the reliability of the output. Previous works showed that a word-dependent naïve …

Time series forecasting can be a complicated problem when the underlying process shows
high degree of complex nonlinear behavior. In some domains, such as financial data …

This work presents a flexible and efficient discriminative training approach for statistical
machine translation. We propose to use the RPROP algorithm for optimizing a maximum …

Tretthet var inntil nylig ikke ansett som en utfordring for stigerør-og brønnhodesystemer.
Belastning på strukturene ble derfor ikke tatt med i design kravene. Siden har utvikling av …

[EN] This thesis work contributes to the field of the {Automatic Speech Recognition}(ASR).
And particularly to the {Interactive Speech Transcription} and {Confidence Measures}(CM) …

Convolutional neural networks (CNNs) are a variant of deep neural networks (DNNs)
optimized for visual pattern recognition, which are typically trained using first order learning …