Estimating Prediction Intervals for Arti cial

Evolving Neural Networks for Chlorophyll a Prediction

This paper studies the application of evolutionary arti cial neural networks to chlorophyll a pre diction in Lake Kasumigaura Unlike previous applications of arti cial neural networks in this eld the architecture of the arti cial neural network is evolved automatically rather than designed man ually The evolutionary system is able to nd a near optimal architecture of the arti cial neural networ...

Platforms for arti cial neural networks Neurosimulators and Performance prediction of MIMD Parallel Systems

Learning to Speculate: Experiments with Arti cial and Real Agents

This paper employs an arti cial agent{based computational approach to understanding and designing laboratory environments in which to test Kiyotaki and Wright's search model of money. The behavioral rules of the arti cial agents are modeled on the basis of prior evidence from human subject experiments. Simulations of the arti cial agent{based model are conducted in two new versions of the Kiyot...

The arti®cial boundary conditions for computing the ̄ow around a submerged body

In this paper we consider the numerical simulations of the steady linearized potential ̄ow around a body which moves in a liquid of ®nite constant depth at constant speed and distance below a free surface. Two vertical segments are introduced as the arti®cial boundary to limit the unbounded physical domain to a bounded computational domain. A series of approximate arti®cial boundary conditions ...

Evolutionary Arti cial Neural Networks 12 Xin

Evolutionary arti cial neural networks (EANNs) [1] result from combinations of arti cial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This article introduces the concept of EANNs, reviews the current state-of-the-art and indicates possible future research directions. X. Yao: Evolutionary Arti cial Neural Networks 1