Analytic chromatography is a physical process whose aim is the separation of the components of a chemical mixture, based on their different aanities for some porous medium through which they are percolated. This paper presents an application of evolutionary recurrent neural nets optimization to the identiication of the internal law of chromatography. New mutation operators involving the paramet...

This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the classical offline grammatical inference with neural networks. The results obtained show that the performance of recurrent networks working online is acceptable when sequences come from finite-state machines or even fro...

We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrixspace models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform compar...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

In Petri net theory, a transition is called live if from every reachable marking there starts a computation in which the transition occurs. Another important property of a transition is the recurrent occurrence of the transition in every computation. In that case, we call the transition recurrent. Though liveness and recurrence of a transition are similar in spirit, there is a big di erence: Li...

We propose a novel architecture for natural language inference. On top of a traditional recurrent neural net with attention architecture, we add memory-based modules, residual connections, and richer word embeddings. With these, we are able to achieve 76.6% accuracy.

in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

In this paper we present a novel architecture for the task of meeting event recognition in meetings that was inspired by the neural field theory. These group actions provide a basis that enables effective browsing and querying in a meeting archive. For our research we used the public available meeting corpus that is described in [3]. This corpus consists in special scripted meetings that were r...

The introduction of new in situ sensing creates the possibility of directly controlling critical process variables in plasma enhanced chemical vapor deposition systems (PECVD). The complexity of this process makes it necessary to develop empirical models of the system dynamics. This paper describes the experimental and numerical procedures for identifying both transfer function and recurrent ne...

A recurrent neural net is described that learns a set of patterns {ξ µ } in the presence of noise. The learning rule is of a Hebbian type, and, if noise would be absent during the learning process, the resulting final values of the weights w ij would correspond to the pseudo-inverse solution of the fixed point equation in question. For a non-vanishing noise parameter, an explicit expression for...