๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Learning contiguity with layered neural networks

โœ Scribed by Sara A. Solla

Publisher
Elsevier Science
Year
1988
Tongue
English
Weight
42 KB
Volume
1
Category
Article
ISSN
0893-6080

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Learning persistent dynamics with neural
Learning persistent dynamics with neural networks
โœ H.A Ceccatto; H.D Navone; H Waelbroeck ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 296 KB

Persistence is one of the most common characteristics of real-world time series. In this work we investigate the process of learning persistent dynamics by neural networks. We show that for chaotic times series the network can get stuck for long training periods in a trivial minimum of the error fun

DESIGN AND LEARNING WITH CELLULAR NEURAL
DESIGN AND LEARNING WITH CELLULAR NEURAL NETWORKS
โœ NOSSEK, JOSEF A. ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 752 KB

The template coefficients (weights) of a CNN which will give a desired performance, can either be found by design or by learning. 'By design' means that the desired function to be performed can be translated into a set of local dynamic rules, while 'by learning' is based exclusively on pairs of inpu

A Sequential Learning Approach for Singl
A Sequential Learning Approach for Single Hidden Layer Neural Networks
โœ Jie Zhang; A.J. Morris ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 722 KB

A sequential orthogonal approach to the building and training of single hidden layer neural networks is presented in this paper. In the proposed method, hidden neurons are added one at a time. The procedure starts with a single hidden neuron and sequentially increases the number of hidden neurons un

Neural networks with quantum architectur
Neural networks with quantum architecture and quantum learning
โœ Massimo Panella; Giuseppe Martinelli ๐Ÿ“‚ Article ๐Ÿ“… 2011 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 154 KB

## Abstract A method is proposed for solving the two key problems facing quantum neural networks: introduction of nonlinearity in the neuron operation and efficient use of quantum superposition in the learning algorithm. The former is indirectly solved by using suitable Boolean functions. The latte