Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to tackle a simple pseudo-natural machine tr...

Both Neural Networks and Finite-State Models have recently proved to be encouraging approaches to Example-Based Machine Translation. This paper compares the translation performances achieved with the two techniques as well as the corresponding resources required. To this end, both Elman Simple Recurrent Nets and Subsequential Transducers were trained to tackle a simple pseudo-natural machine tr...

With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting fluctuations is usually challenging due to nonlinear and nonstationary time series of prices. The Elman recurrent network renowned for its capability dealing with dynamic information, which made it successful application predicting. We developed hybrid approach combined factorization ...

In feedforward networks, signals ow in only one direction without feedback. Applications in forecasting, signal processing and control require explicit treatment of dynamics. Feedforward networks can accommodate dynamics by including past input and target values in an augmented set of inputs. A much richer dynamic representation results from also allowing for internal network feedbacks. These t...

In this paper we describe how an actor critic rein forcement learning agent in a non Markovian domain nds an optimal sequence of actions in a totally model free fashion that is the agent neither learns transitional probabilities and associated rewards nor by how much the state space should be augmented so that the Markov prop erty holds In particular we employ an Elman type re current neural ne...

The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s...

In this paper, an artificial intelligent tool is proposed using fuzzy logic (FL) and recurrent neural networks (RNN) for definition and forecast of patient’s clinical condition. The fuzzy logic-based proposed first phase of the tool permits the analysis of the current state of the patient, which allows the training of the artificial neural network. In the second phase, two Elman networks Multi ...