Add to ORKGThis work investigates the representational and inductive capabili-ties of time-delay neural networks (TDNNs) in general, and of two subclasses of TDNN, those with delays only on the inputs (IDNN), and those which include delays on hidden units (HDNN). Both ar-chitectures are capable of representing the same class of languages, the definite memory machine (DMM) languages, but the delays on the hidden units in the HDNN helps it outperform the IDNN on problems composed of repeated features over short time windows.
A recurrent neural network (RNN), in which each unit has serial delay elements, is proposed for memo...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
A novel approach for estimating constant time delay through the use of neural networks (NN) is intr...
Abstract-In this work, we characterize and contrast the capabilities of the general class of time-de...
This thesis studies various issues related to artificial neural networks for pattern recognition and...
A procedure for pre-processing non-stationary time series is proposed for modelling with a time-dela...
There has been a lot of interest in the use of discrete-time recurrent neural nets (DTRNN) to learn ...
AbstractSuccessive generations of artificial neural networks have leveraged their multiplicity of co...
We investigate the learning of deterministic finite-state automata (DFA's) with recurrent netwo...
A new class of temporal associative neural network, called a finite state network (FSN), is presente...
A finite automaton --- the so-called neuromaton, realized by a finite discrete recurrent neural netw...
The authors propose a scheme that maps a time-delay neural network (TDNN) into a neurocomputer calle...
The authors develop a parallel structure for the time-delay neural network used in some speech recog...
There has been a lot of interest in the use of discrete-time recurrent neu-ral nets (DTRNN) to learn...
International audienceUnderstanding the influences between components of dynamical systems such as b...
A recurrent neural network (RNN), in which each unit has serial delay elements, is proposed for memo...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
A novel approach for estimating constant time delay through the use of neural networks (NN) is intr...
Abstract-In this work, we characterize and contrast the capabilities of the general class of time-de...
This thesis studies various issues related to artificial neural networks for pattern recognition and...
A procedure for pre-processing non-stationary time series is proposed for modelling with a time-dela...
There has been a lot of interest in the use of discrete-time recurrent neural nets (DTRNN) to learn ...
AbstractSuccessive generations of artificial neural networks have leveraged their multiplicity of co...
We investigate the learning of deterministic finite-state automata (DFA's) with recurrent netwo...
A new class of temporal associative neural network, called a finite state network (FSN), is presente...
A finite automaton --- the so-called neuromaton, realized by a finite discrete recurrent neural netw...
The authors propose a scheme that maps a time-delay neural network (TDNN) into a neurocomputer calle...
The authors develop a parallel structure for the time-delay neural network used in some speech recog...
There has been a lot of interest in the use of discrete-time recurrent neu-ral nets (DTRNN) to learn...
International audienceUnderstanding the influences between components of dynamical systems such as b...
A recurrent neural network (RNN), in which each unit has serial delay elements, is proposed for memo...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
A novel approach for estimating constant time delay through the use of neural networks (NN) is intr...