Recurrent neural networks are dynamical network structures which have the capabilities of processing and generating temporal information. To our knowledge the earliest neural network model that processed temporal information was that of MeCulloch and Pitts [McCulloch43]. Kleene [Kleene56] extended this work to show the equivalence of finite automata and McCulloch and Pitts' representation of ne...

neurilemmomas are benign tumors of peripheral nerve sheath schwann cells. one of the variants of neurilemmoma is the ancient type of neurilemmoma characterized by degenerative features such as cystic degeneration, calcification, hemorrhage and hyalinization which could be easily misdiagnosed. their occurrence in oral cavity is extremely rare and intraosseous type occurring in maxilla is exceedi...

In this paper we prove that the relations P = NP and P 6= NP relativise to the deterministic / non-deterministic artificial recurrent neural net (ARNN ) with real weights (informally considered as oracles in [1] and [2]). Although, in the nineties, a dozen of papers were written on the ARNN model, some introducing computation via neural nets with real weights and some introducing non-determinis...

Conventional speech recognition system is constructed by unfolding the spectral-temporal input matrices into one-way vectors and using these vectors to estimate the affine parameters of neural network according to the vector-based error backpropagation algorithm. System performance is constrained because the contextual correlations in frequency and time horizons are disregarded and the spectral...

Determining the architecture of a neural network is an important issue for any learning task. For recurrent neural networks no general methods exist that permit the estimation of the number of layers of hidden neurons, the size of layers or the number of weights. We present a simple pruning heuristic that significantly improves the generalization performance of trained recurrent networks. We il...

This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure o...

The Time-Slicing paradigm is a newly developed method for the training of neural networks for speech recognition. The neural net is trained to spot the syllables in a continuous stream of speech. It generates a transcription of the utterance, be it a word, a phrase, etc. Combined with a simple error recovery method the desired units (words or phrases) can be retrieved. This paradigm uses a recu...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...