Computers are typically not very good at tasks at which humans excel, such as image and speech recognition, reasoning, understanding, and acting in the face of uncertainty. This difference cannot be due directly to a lack of speed, since a computer actually manipulates data thousands of times faster than neurons in the brain. However, computer processors have a structure that is very different ...

The nature of iterative learning on a randomized initial architecture, such as backpropagation training of a multilayer perceptron, is such that precise replication of a reported result is virtually impossible. The outcome is that experimental replication of reported results, a touchstone of “the scientific method,” is not an option for researchers in this most popular subfield of neural comput...

According to the statistical interpretation of quantum theory, quantum computers form a distinguished class of probabilistic machines (PMs) by encoding n qubits in 2n pbits. This raises the possibility of a large-scale quantum computing using PMs, especially with neural networks which have the innate capability for probabilistic information processing. Restricting ourselves to a particular mode...

Neural networks are systems of interconnected processors mimicking some of the brain functions. After a rapid overview of neural computing, the thermodynamic formalism of the learning procedure is introduced. Besides its use in introducing eecient stochas-tic learning algorithms, it gives an insight in terms of information theory. Main emphasis is given in the information restitution process ; ...

Neural Network ensemble is a learning paradigm where a collection of finite number of neural networks is trained for the same task. It is understood that the generalization ability of neural networks, i.e., training many neural networks and then combining their predictions. ANN ensemble techniques have become very popular amongst neural network practitioners in a variety of ANN application doma...

The application of neural computing to the problem of matching in production systems is addressed. The computation time required by this problem can be significantly reduced by using the massive parallelism and pattern recognition capabilities available through neural computing. A new neural computing model, called here the ProNet, is introduced and explained in detail. The ProNet is applied to...