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However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed.
Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible. Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks.
Being a flexible model building method, the artificial neural network is an ideal tool to construct the complex relationship between the input and the output parameters accurately.
Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models.
An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems.
Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.
The experimental results prove that, in such tasks, our algorithm can build, in a completely automated way, neural network topologies able to outperform classic neural network models designed by hand.
This package makes it possible to build and train an artificial neural network model that approximates a collective variable.
Journal ArticleDOI
Tadashi Hattori, Shigeharu Kito 
07 Apr 1995-Catalysis Today
90 Citations
This article introduces a novel information science technique, an artificial neural network, which will possibly be a powerful tool for catalyst development.
In this paper, we proposed a bionic way to implement artificial neural network through construction rather than training and learning.

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