The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth's atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governme...

This thesis presents work in three main directions of the automatic speech recognition field. The work within two of these – dynamic decoding and hybrid HMM/ANN speech recognition – has resulted in a real-time speech recognition system, currently in use in the human/machine dialogue demonstration system WAXHOLM, developed at the department. The third direction is fast unsupervised speaker adapt...

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilisti...

In this paper we present a model free hybrid visual servoing system. The “model free” term refers to the system with the unknown kinematics model that has to be estimated on-line, while “hybrid” specifies the visual controller architecture. The proposed system has a conventional Jacobian estimation part necessary for control output generation and it is supplemented with an additional adaptive n...

3 Introduction Introduction This paper presents a hybrid intelligent system for This paper presents a hybrid intelligent system for electrical load forecast, named by PREVER. electrical load forecast, named by PREVER. Artificial Neural Networks (ANN) were combined with Artificial Neural Networks (ANN) were combined with Heuristic Rules to create the system. Heuristic Rules to create the system....

The nonlinear load such as UPS, fan controller, mobile charger is distorting the supply current waveform. This current harmonics derates the power system equipment. To avoid this current harmonics, filters namely passive filter / active filter /hybrid filters are introduced in the supply line. In this paper the overall view of the performance of the various filters are studied and the performan...

This work proposes an attempt to control the navigation of a wall following mobile robot using a hybrid metaheuristic which is a combination of two different population-based heuristics such as Gravitational Search (GS) and Particle Swarm Optimization (PSO). The hybrid metaheuristic is called GSPSO which is used to train a multi-layered Artificial Neural Network (ANN) with the available benchma...

This paper presents a hybrid ANN/HMM syllable recognition module based on vowel spotting. An advanced multi-level vowel spotting method is used to achieve minimum vowel loss and accurate detection of the vowel location and duration. Discrete Hidden Markov Models (DSHMM), Multi Layer Perceptrons (MLP) and Heuristics (HR) are used for this purpose. A hybrid ANN/HMM technique is then used to recog...

Energy consumption time series consists of complex linear and non-linear patterns and are difficult to forecast. Neither autoregressive integrated moving average (ARIMA) nor artificial neural networks (ANNs) can be adequate in modeling and predicting energy consumption. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linea...