Recent results in hybrid neural networks using extended versions of the core method have shown that we can use background knowledge to guide back-propagation learning. This paper further explores this ideas by adding numeric functions to the encoded knowledge and using the traditional recursive Elman neural network model. An illustration of the properties of these neural networks will be used t...

1997 The following supplement to the original (1997) field guide is available:

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selecti...

The aim of this section is to provide objective works.com. Microsoft Windows, UNIX, and information to guide choices in both scientific Macintosh versions are available. Neural Netand business applications. Anyone wishing to work Toolbox 3.0 requires MATLAB. contribute a review should write to the editor at the address below. The suggested outline for a Neural Network Toolbox authors software r...

This paper introduces a novel framework for learning data science models by using the scientific knowledge encoded in physics-based models. This framework, termed as physicsguided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, we present a novel class of learni...

This paper presents our attempt to automatically define feedforward neural networks using genetic programming. Neural networks have been recognized as powerful approximation and classification tools. On the other hand, the genetic programming has been used effectively for the production of intelligent systems, such as the neural networks. In order to reduce the search space and guide the search...

Motion vision is an ancient faculty, critical to many animals in a range of ethological contexts, the underlying algorithms of which provide central insights into neural computation. However, how motion cues guide behavior is poorly understood, as the neural circuits that implement these computations are largely unknown in any organism. We develop a systematic, forward genetic approach using hi...