In this chapter, we shall present a technique for automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space (AS), which is a family of ANNs. The AS can be formed according to the problem in hand encapsulating ...
1) evolutionary artificial neural network(EANN) 进化神经网络(EANN)2) evolutionary neural network 进化神经网络 1. Prediction of cutting tool life based on evolutionary neural network; 基于进化神经网络的刀具寿命预测 2. Time serial model of rock burst based on evolutionary neural network; 冲击地...
1) evolutionary artificial neural network 进化人工神经网络 2) evolutionary model for ANN 进化人工神经网络模型 3) optimized artificial neural network 优化人工神经网络 1. The application ofoptimized artificial neural networkto refraction static correction; ...
Evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of Evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the difficulties in design and development of DNNs, ENNs can optimize and supplement deep lear...
Automated design of artificial neural networks by evolutionary algorithms (neuroevolution) has generated much recent researchboth because successful approaches will facilitate wide-spread use of intelligent systems based on neural networks,and because it will shed light on our understanding of how "real" ...
In this thesis, I present the GALSINE learning algorithm for automated learning on structured data. This is a genetic algorithm for artificial neural networks, modelled on evolution in nature, to allow effective machine learning while simultaneously limiting the amount of human intervention necessary.年...
Artificial neural networks have the ability to approximate arbitrary linear or nonlinear mapping by means of learning. Because, of the learning ability, the neural networks have been developed to compensate for the nonlinearities and uncertainties in design of control systems. Particularly, the RBF-NNs...
Artificial Neural NetworksOptimizationAlthough a great amount of algorithms have been devised to train the weights of a neural network for a fixed topology, most of them are hillclimbing procedures, which usually fall in a local optimum; that is why results obtained depend to a great extent on ...
Intelligent Optimization of Reinforcement Design Using Evolutionary Artificial Neural Network for the Muzishu Landslide Based on GISChapter First Online: 01 January 2009 pp 449–463 Cite this chapter Landslide Disaster Mitigation in Three Gorges Reservoir, China ...
USING A DYNAMIC ARTIFICIAL NEURAL NETWORK FOR FORECASTING THE VOLATILITY OF A FINANCIAL TIME SERIES Using a dynamic artificial neural network for forecasting the volatility of a financial time series, Volatility forecast, prediction, nonlinear models, ... JD Velásquez,S Gutiérrez,CJ Franco - 《Revis...