The authors propose a scheme that maps a time-delay neural network (TDNN) into a neurocomputer called EMIND-II with the wavefront toroidal mesh-array structure. The authors also define the programming model of this array and derive the parallel algorithms concerning the TDNN for the EMIND-II. For demonstration purposes, this neurocomputer is applied to word recognition.X11sciescopu
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recog...
Temporal coding is one approach to representing information in spiking neural networks. An example o...
Neural network research, long focused on static pattern recognition, is now extended to spatiotempor...
The authors develop a parallel structure for the time-delay neural network used in some speech recog...
Abstract—In this paper, we develop a parallel structure for the time-delay neural network used in so...
This thesis studies various issues related to artificial neural networks for pattern recognition and...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
An analog model neural network that can solve a general problem of recognizing patterns in a time-de...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
This thesis describes the design and implementation of two pattern recognition systems on field-prog...
Dynamic analysis of temporally changing signals is a key issue in real-time signal processing and un...
The Adaptive Time-delay Neural Network (AT N N), a paradigm for training a nonlinear neural network ...
Abstract-In this work, we characterize and contrast the capabilities of the general class of time-de...
This work investigates the representational and inductive capabili-ties of time-delay neural network...
A novel approach for estimating constant time delay through the use of neural networks (NN) is intr...
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recog...
Temporal coding is one approach to representing information in spiking neural networks. An example o...
Neural network research, long focused on static pattern recognition, is now extended to spatiotempor...
The authors develop a parallel structure for the time-delay neural network used in some speech recog...
Abstract—In this paper, we develop a parallel structure for the time-delay neural network used in so...
This thesis studies various issues related to artificial neural networks for pattern recognition and...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
An analog model neural network that can solve a general problem of recognizing patterns in a time-de...
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns ...
This thesis describes the design and implementation of two pattern recognition systems on field-prog...
Dynamic analysis of temporally changing signals is a key issue in real-time signal processing and un...
The Adaptive Time-delay Neural Network (AT N N), a paradigm for training a nonlinear neural network ...
Abstract-In this work, we characterize and contrast the capabilities of the general class of time-de...
This work investigates the representational and inductive capabili-ties of time-delay neural network...
A novel approach for estimating constant time delay through the use of neural networks (NN) is intr...
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recog...
Temporal coding is one approach to representing information in spiking neural networks. An example o...
Neural network research, long focused on static pattern recognition, is now extended to spatiotempor...