STRACTION FOR DISCRETE EVENT SYSTEMS USING NEURAL NETWORKS AND SENSITIVITY INFORMATION Christos G. Panayiotou Christos G. Cassandras Department of Manufacturing Engineering Boston ...
Christos G. Panayiotou, Christos G. Cassandras, We...
An asynchronous PDM (Pulse-Density-Modulating) digital neural network system has been developed in our laboratory. It consists of one thousand neurons that are physically intercon...
This paper presents an algorithm for extract ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
Experimental data show that biological synapses behave quite differently from the symbolic synapses in common artificial neural network models. Biological synapses are dynamic, i....
We suggest a recurrent neural network (RNN) model with a recurrent part corresponding to iterative function systems (IFS) introduced by Barnsley 1] as a fractal image compression ...
In this paper we report about an investigation in which we studied the properties of Bayes' inferred neural network classifiers in the context of outlier detection. The proble...
: This paper describes a system, which integrates Neural Network (NN) models into adaptation circle of Case-based Reasoning (CBR) system. Neural Network supported adaptation can pr...
This paper reports the results of experiments in complex Arabic phonetic features identification using a rulebased system (SARPH) and modular connectionist architectures. The firs...
In this paper a new approach for approximation problems involving only few input and output parameters is presented and compared to traditional Backpropagation Neural Networks (BP...
A system based on the charge-discharge characteristics of the neural synapses of the visual path, is shortly introduced. The proposed system uses the LSR (length/speed ratio) descr...