Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more acc...

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the basе of system’s state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simu...

This study proposed a model-based robust fault detection (RFD) method using soft computing techniques. Robust detection of the possible realistic incipient faults of an industrial gas turbine engine in steady-state conditions is mainly centered. For residual generation a bank of Multi-Layer perceptron (MLP) models, is used, Moreover, in fault detection phase, a passive approach based on Modelli...

This paper combines new and known results in a single convenient source for anyone interested learning about quasi-linear functionals on locally compact spaces. We define singly generated subalgebras different settings study signed positive functionals. Quasi-linear are, general, nonlinear, but linear subalgebras. The gives representation theorems C c (X), bounded 0 (X) space, C(X) space. There...

In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian brain, the limbic system processes emotional stimulus and consists of two main components: Amygdala and Orbitofrontal Cortex (OFC). Here, we propose a learning algorithm for...