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Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer

Publications associées (59)

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A Method for All-Positive Optical Multilayer Perceptrons

The most promising approaches for optical neural networks are based on intensity encoding. However, a serious drawback of intensity encoding is the lack of negative values and optical subtraction, which are essential for rendering neural networks useful. T ...

IEEE1996

Heterogeneous artificial neural network for short term electrical load forecasting

Short-term electrical load forecasting is a topic of major interest for the planning of energy production and distribution. The use of artificial neural networks has been demonstrated as a valid alternative to classical statistical methods in term of accur ...

1996

The Effects of Optical Thresholding in Backpropagation Neural Networks

Sigmoid-like activation functions implemented in analog hardware differ in various ways from the standard sigmoidal function as they are asymmetric, truncated, and have a non-standard gain. It is demonstrated how one can adapt the backpropagation learning ...

EC2 & Cie1995

A feedback analysis of perceptron learning for neural networks

Ali H. Sayed

This paper provides a time-domain feedback analysis of the perceptron learning algorithm. It studies the robustness performance of the algorithm in the presence of uncertainties that might be due to noisy perturbations in the reference signal or to modelin ...

1995

Heterogeneous artificial neural network for short term electrical load forecasting

Short term electrical load forecasting is a topic of major interest for the planning of energy production and distribution. The use of artificial neural networks has been demonstrated as a valid alternative to classical statistical methods in term of accur ...

1995

Results on the Steepness in Backpropagation Neural Networks

The backpropagation algorithm is widely used for training multilayer neural networks. In this publication the steepness of its activation functions is investigated. In specific, it is discussed that changing the steepness of the activation function is equi ...

1994

Adaptive Multilayer Optical Neural Network Design

An adaptive multilayer dual-wavelength optical neural network design with all-optical forward propagation, based on a large number of modifiable optical interconnections and a special weight discretization algorithm to compensate for em noise, is described ...

IDIAP1994

Comparison of dynamic load modeling using neural network and traditional method

The representation of load dynamic characteristics remains an area of great uncertainty and it becomes a limiting factor of power systems dynamic performance analysis. A major difficulty, both for component-based and measurement-based methods, is the lack ...

1993

Local Learning Algorithm For Optical Neural Networks

Demetri Psaltis

An anti-Hebbian local learning algorithm for two-layer optical neural networks is introduced. With this learning rule, the weight update for a certain connection depends only on the input and output of that connection and a global, scalar error signal. The ...

1992